Research proposal

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Answer the following questions

· Differences between qualitative & quantitative research

· Describe the methods in qualitative research

· Sampling techniques in qualitative research

· Sampling in quantitative research

· Cite references from Classical authors in research methodology

N.B. Referencing using APA type of referencing 6th edition (For intext citation and references/bibliography)

EFFAH SAMPSON Sample thesis.pdf

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KWAME NKRUMAH UNIVERSITY OF SCIENCE AND TECHNOLOGY

KUMASI

DEPARTMENT OF ACCOUNTING AND FINANCE

TOPIC: ASSESSING THE PUBLIC PROCUREMENT ACT IN THE

MANAGEMENT OF DRUGS AT SUHUM GOVERNMENT HOSPITAL

BY

EFFAH SAMPSON

(PG2140614)

A THESIS SUBMITED TO THE DEPARTMENT OF ACCOUNTING AND

FINANCE, KWAME NKRUMAH UNIVERSITY OF SCIENCE AND

TECHNOLOGY SCHOOL OF BUSINESS IN PARTIAL FULFILMENT OF THE

REQUIREMENT FOR THE AWARD OF

MASTER OF BUSINESS ADMINISTRATION

ACCOUNTING OPTION

NOVEMBER, 2015

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DECLARATION

I declare that, this thesis is based on a study undertaken by me personally. Where other

persons ideas have been used or utilized, this have been duly acknowledged by me.

I wish to state that, this thesis does not contain materials which have been used to earn a

degree from this prestigious university or any accredited institution.

EFFAH SAMPSON ……………………… …………………

STUDENT NAME SIGNATURE DATE

CERTIFIED BY;

MR AGANA AGADEAGRE JOSEPH ……………………. …………………

SUPERVISOR SIGNATURE DATE

CERTIFIED BY;

DR. K. O. APPIAH …………………… …………………

HEAD OF DEPARTMENT SIGNATURE DATE

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ABSTRACT

The goal of this study is to improve the efficiency of procurement and management of

logistics (drugs) in a District Hospital in Ghana. The study adopted a case study approach in

which Suhum Government Hospital; a district health facility in the Suhum Municipality was

used as the research location.

The study employs both qualitative and quantitative research approaches through the use of

questionnaires administered to sixty- three (63) employees. Interviews were also conducted

for 4 managers involved in procurement activities. Microsoft excel spread- sheet was used to

analyze the quantitative data. Also, respondents’ opinions were grouped under sub- headings

reflecting the research objectives.

In this research, the respondents were of the opinion that, they were aware of the existence

of the Public Procurement Act which reference could be made to serve as a guide. However,

they could not express how they heard about it. The study indicates that respondents knew

about the Public Procurement Act 2003 (Act 663) to be an Act of parliament and also

reveals that the Act seeks to achieve right procurement procedures, judicious use of

government funds, equity and fairness and value for money. The study also indicates that

Suhum Government hospital does not have spacious storage facility though all items in the

store are listed in stores receipt vouchers. Furthermore, the study reveals that, internal

auditor does store quantity checks before items are received into stores. The study shows

that value for money, accountability, cost reduction and transparency with reference to

health delivery have been achieved since the inception of the Public Procurement Act 2003

(Act 663).

The Ministry of Health or Ghana Health Service should ensure that there is avoidance of

bureaucratic corruptions, availability of funds, institution of proper internal controls and

training sessions for procurement staffs to ensure that there is strict adherence to the Public

Procurement Act.

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DEDICATION

This research work is dedicated to my lovely wife Comfort Yaa Asantewaa and my two

lovely children namely; Ama Asabea Effah and Kofi Nyamikye Effah for their Physical and

spiritual Support

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ACKNOWLEDGEMENT

I would like to first express my profound gratitude to the Almighty God for the wisdom, life,

strength and the chance to sail through my studies. A lot of people offered me assistance in

various useful ways for a successful completion of my thesis and I wish to express my

upmost gratitude to all of them. I am highly indebted to Mr. Collins O. Mintah, my thesis

supervisor, for his sincere care and guidance during the period of this study. I would like to

express my sincere thanks to the department of accounting and finance as well as all

lecturers who thought me in various disciplines.

Certain people were of very importance to me not only during the development of my thesis

proposal but also in my academic endeavors. They are; Dr. Emmanuel Tetteh Ashong, the

Medical Superintendent, Suhum Government Hospital, Victor Owusu, Health Services

Administrator, Suhum Government Hospital, Dr. Sammuel Buabeng Frimpong, past

Medical Superintendent, Kwahu Government Hospital, Mrs. Bernice Omari – Siaw,

Pharmacist in-charge Kwahu Government Hospital, Philip Opoku-Amponsah , Pharmacist

in-charge Suhum Government Hospital, Sethina Adu- Boache, Insurance unit Kwahu

Government Hospital, Miss Vera Boateng, Insurance Claims Unit, Kwahu Government

Hospital, Richard Osei, Verification Unit, Kwahu Government Hospital, Ofori Emmanuel,

Boadu Godfred, Kwasi Boakye Otchere, all national service personnel attached to the

Insurance claims unit of Kwahu Government Hospital and Mr. Richard Nimako a lecturer

with Business Department (PUC Abetifi, Kwahu) for their immense support .

My salute goes to people whose work I used as references

Last but not the least; I sincerely thank Mr. Agadiak Sharif, who spent his precious time in

typing of the work and Miss Faustina Ohenewaa, Center for Continuous Education-

University of Cape- Coast who gave me moral support.

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TABLE OF CONTENTS

DECLARATION ..................................................................................................................... ii

ABSTRACT ........................................................................................................................... iii

DEDICATION ........................................................................................................................ iv

ACKNOWLEDGEMENT ....................................................................................................... v

TABLE OF CONTENTS ....................................................................................................... vi

LIST OF FIGURES ................................................................................................................ ix

LIST OF TABLES ................................................................................................................... x

LIST OF ABREVIATIONS ................................................................................................... xi

CHAPTER ONE ...................................................................................................................... 1

INTRODUCTION ................................................................................................................... 1

1.1 Background of the Study................................................................................................ 1

1.2 Statement of the problem ............................................................................................... 3

1.3 Research objectives ........................................................................................................ 4

1.4 Research Questions ........................................................................................................ 4

1.5 significance of the study ................................................................................................ 5

1.6 Scope/Limitations of the study ...................................................................................... 5

1.7 Organization of the study ............................................................................................... 6

CHAPTER TWO ..................................................................................................................... 7

LITERATURE REVIEW ........................................................................................................ 7

2.0 Introduction .................................................................................................................... 7

2.1 Empirical Literature ....................................................................................................... 7

2.2 Meaning of Procurement .............................................................................................. 10

2.3 Logistics Management ................................................................................................. 10

2.5 The Reform Program ................................................................................................... 12

2.6 Procurement Cycle ....................................................................................................... 13

2.6.1 Planning .................................................................................................................... 13

2.6.2 Sourcing .................................................................................................................... 14

2.6.3 Storage ................................................................................................................. 15

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.2.6.4 Evaluation ................................................................................................................ 15

2.6.5 Distributions .............................................................................................................. 15

2.6.6 Disposal ..................................................................................................................... 15

2.7 Procurement Objectives ............................................................................................... 16

2.7.1 Professionalism ......................................................................................................... 16

2.7.2 Transparency ............................................................................................................. 16

2.7.3 Value for Money (VFM) ........................................................................................... 16

2.7.4 Competitiveness ........................................................................................................ 17

2.7.5 Accountability ........................................................................................................... 17

2.7.7 Efficiency .................................................................................................................. 17

2.8 The Five Rights Under Public Procurement Act ......................................................... 18

2.8.1 The right quality .................................................................................................. 18

2.8.2 The right quantity ...................................................................................................... 18

2.8.3 The right time (To buy and to deliver) ...................................................................... 18

2.8.5 The right price ..................................................................................................... 19

2.8 Notification of Unsuccessful Tenderers ....................................................................... 19

2.9 Controlling and Surpervising the Procurement Cycle ................................................. 19

2.10 Ensuring quality of the Product ................................................................................. 20

2.11 The Outcome of Effective Procurement System ........................................................ 20

2.12 Conceptual Framework .............................................................................................. 22

CHAPTER THREE ............................................................................................................... 23

RESEARCH METHODOLOGY .......................................................................................... 23

3.1 Introduction ............................................................................................................. 23

3.2 Research Design ........................................................................................................... 23

3.3 Population .................................................................................................................... 25

3.4 Sample and Sampling Procedures ................................................................................ 25

3.5 Data and data collection procedures ............................................................................ 27

3.6 Scientific credibility ..................................................................................................... 30

3.7 Analysis of data:........................................................................................................... 31

3.8 Ethical consideration: ................................................................................................... 31

3.9 Profile of Suhum Government Hospital....................................................................... 32

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CHAPTER FOUR ................................................................................................................. 33

RESLUTS AND DISCUSSIONS.......................................................................................... 33

4.1 Introduction .................................................................................................................. 33

4.2 Background Attributes of Respondents in Percentages ............................................... 33

4.3 Department of Respondents ......................................................................................... 34

4.4 Academic Qualification of Respondents ...................................................................... 35

4.5 Existence of the Procurement Act (PPA) ..................................................................... 36

4.6 How Respondents got to know about the Public Procurement Act ............................. 37

4.7 What the Public Procurement Act, 2003 (Act 663) Stands For. .................................. 39

4.8 Access to a Copy of the Public Procurement Act? ...................................................... 40

4.9 Reading Through or Making Reference to the Public Procurement Act? ................... 41

4.10 What does the Public Procurement Act Seek to Achieve? ........................................ 44

4.11 The PPA is Useful in the Conduct of Business of the Hospital ................................. 45

CHAPTER FIVE ................................................................................................................... 51

SUMMARY OF FINDINGS, CONCLUSIONS AND RECOMMENDATIONS ................ 51

5.1 Summary of Findings ................................................................................................... 51

5.2 Conclusion ................................................................................................................... 54

5.3 Recommendations ........................................................................................................ 55

REFERENCES ...................................................................................................................... 58

APPENDIXES ....................................................................................................................... 64

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LIST OF FIGURES

Figure 1. ................................................................................................................................. 13

Figure 2 Conceptual Framework ........................................................................................... 22

Figure. 3: Gender of the Respondents ................................................................................... 33

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LIST OF TABLES

Table 1: .................................................................................................................................. 34

Table 2 ................................................................................................................................... 35

Table 3 ................................................................................................................................... 36

Table 4 ................................................................................................................................... 38

Table 5 ................................................................................................................................... 39

Table 6 ................................................................................................................................... 40

Table 7 ................................................................................................................................... 41

Table 8 ................................................................................................................................... 44

Table 9 ................................................................................................................................... 45

Table 10 (A) Value For Money; ............................................................................................ 46

Table 10(B) Accountability ................................................................................................... 47

Table 10 (C) Cost Reductions ................................................................................................ 48

Table 10 (D) Quality Of Service Delivery. ........................................................................... 49

Table 10 (E) Transparency .................................................................................................... 50

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LIST OF ABREVIATIONS

GDP - Gross Domestic Product

SGH - Suhum Government Hospital

PPA - Public Procurement Act

MOH - Ministry Of Health

GHS - Ghana Health Service

ATFRR - Accounting, Treasury and Financial Reporting Rule

BMC - Budget Management Center

FAR - Financial Administration Regulations

LMIS - Logistics Management Information Systems

USIAD - United State Agency for International Development

MMDA’s - Metropolitan, Municipal, Department and Agencies

VFM - Value for Money

WHO - World Health Organization

HMT - Hospital Management Team

NHIA National Health Insurance Authority

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CHAPTER ONE

INTRODUCTION

1.1 Background of the Study

Every institution, being it government or non - governmental, profit or non-profit making,

undertakes a certain level of purchases. Public procurement takes between the ranges of 50-

70% of the national budget of the country, 14% of Gross Domestic Product (GDP) and 24%

of total imports brought into the country (World Bank CPAR 2003)

As institutions strives to accept cost and focus on their core competencies, procurement or

the purchasing activity come under strict supervision. It is now crucial than before for

procurement to accept timely and cost effective procedures.

Public procurement can be explained as the steps by which government follows to make

purchases of goods, services and works using funds generated by states through imposition

of taxes, loans solicited by the state and finds paid into the consolidated account by the

various state agencies. It involves organizing, directing, planning, controlling, invitation of

offers, evaluating offers and contract management.

With regards to the activities using Suhum Government Hospital as a case study, it was

established that, the procurement department cannot effectively perform without taking into

consideration the purchasing process. It is not out of the blue that the government has

expressed its intentions supported by the Public Procurement 2003 (Act 663).

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With reference to these indications, it is important to look into the conditions at Suhum

Government Hospital to offer a well – to - do contributions as well as recommendations for

expected or accepted results.

From a cost reduction base, stores management contributes to maximize the profit earnings

of an institution like Suhum Government Hospital. It is important for every store department

to give out efficient and provision of good services to the various units within the institution

for smooth flow of drugs within the hospital so as to meet targeted objectives. With this, It is

essential to institute well-establish and professionally controlled stores in the institution.

Logistics management in the health sector involves the procedures used to manage the

supply of health commodities, including essential medicines, non-medicine consumables

such as medical supplies and disposables, dental and laboratory supplies (Logistics

Management of Public Sector Commodities in Ghana, GHS, and July, 2002)

Continuous availability of basic quality logistics in health facilities is very key for their

proper functioning. Effective and efficient management of logistics are therefore very

crucial in health facilities.

The Ministry of Health (MOH) and the Ghana Health Service (GHS) have come out with

rules and regulations to manage drugs, procurement of quality and affordable drugs (value

for money) with the accepted specifications and appropriate storage facilities among others.

The MOH Accounting, Treasury and Financial Reporting Rules and obliged Manual 2010

page 58, Budget Management Centers (BMCs) are enjoined by rules, regulations 182 and

183 of Financial Administration Regulations( FAR 2004), to fuse sound inventory controls

in their management.

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1.2 Statement of the problem

Due to most District Hospitals inability to raise enough revenue internally to settle their

numerous debts coupled with lack of transparency when it comes to procurement of drugs

and National Health Insurance Authority (NHIA) inability to reimburse the various hospitals

in the country on time. This has affected most hospitals when it comes to generation of

funds to procure drugs and continuing auditors report lamenting on abuse of purchasing

procedures that has been made clear through the report of Public Accounts Committee of

Ghana. Government took a decision to come out with steps or procedures to curtail

procurement irregularities, some of which emanate from bureaucratic corruption which SGH

is not an exemption. This contributed into government passing the procurement bill into an

Act in the year 2003 (Act 663) to bring sanity in the procurement prosses.

As it stands now Suhum, Government Hospital has many difficulties when it comes to

procurement and management of drugs due to;

 Lack of qualified store keepers

 Inadequate storage facilities

 The challenges of having to follow the lay down procurement procedures

 The random manner in which management orders for goods and services in the mist

of the above mentioned contentious statement, it is important to look into the

situation of Suhum Government Hospital and to come out with necessary control

measures and to establish recommendations for expected outcome.

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1.3 Research objectives

The following objectives are expected to be achieved at the end of the research;

Main objectives

To examine the process of procurement and management of drugs consumables at

Suhum Government Hospital in the Suhum municipality and to see whether they are

effective and efficient.

Specific objectives

1) To assess the level of awareness of employees at Suhum Government Hospital of

the Public Procurement Act 2003 (Act 663)

2) To ascertain the extent to which the stores procedures of Suhum Government

Hospital comply with the Public Procurement Act.

3) To ascertain the impact or the effect of the Public Procurement Act on the

operations of the Suhum Government Hospital.

1.4 Research Questions

1) What is the level of awareness of the Public Procurement Act of employees at

Suhum Government Hospital?

2) What is the extent of compliance of the stores procedure of Suhum

Government Hospital with the Public Procurement Act (PPA)?

3) What is the impact/effect of the Public Procurement Act on the operations of

the Suhum Government Hospital/

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1.5 significance of the study

The outcome of the study could be of use to the government of Ghana, Ministry of Health

(MOH), Ghana Health Service (GHS), Regional and District Hospitals in Ghana that have

expressed willingness to the effective and efficient use of the PPA Act 2003 (Act 663). It is

expected therefore that the end result from the research when published in the print media,

Medical journals and economic magazines would have a significant influence in various

disciplines to health think thanks, medical officers, auditors, accountants, administrators,

pharmacists, procurement officers, and the entire staffs at the various Hospitals in Ghana,

and the Health sector as a whole.

Notwithstanding the outcome, the research will give way for future studies in other aspects

of procurement and management of logistics (drugs) to be used by hospital managers in

Ghana.

1.6 Scope/Limitations of the study

Ministry of Health in collaboration with Ghana Health Service has various Health facilities

in the Eastern Region of Ghana. However, due to lack of time constraints, the SGH is

chosen as the area of the study.

The chosen sample may not truly reflect that of a district Hospital and that may lead to

sample inconsistencies.

The personal interviews and questionnaires may show case inconsistencies in the research

study upon inability to derive appropriate responses from questionnaire administration and

interviews. This may lead to the inability of using the findings of the research/ work to

generalize an outcome.

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1.7 Organization of the study

This research/ study falls under the broad headings of Introduction, Literature review and

Conceptual frame work, Methodology, Analysis of the data collected and finally Summary

of findings, Conclusion, and Recommendations.

Chapter one involves the introduction of the research, background of the study, statement of

the problem, objectives of the study, research questions, significance of the study and how

the study was organized.

Chapter two dealt with the literature review of the research/ Study where other peoples

work, idea, findings and opinions contributed to establish the nature and significance of the

research topic are located.

Chapter three focuses on the methodology adopted of the research/ study in terms of

research design, population, sample and sampling procedures, data and data collection

procedures, pilot-testing, scientific credibility, analysis of data, limitations of the study,

ethical consideration, profile of SGH and overview of activities.

Chapter four centered on data analysis, discussions and findings of the study. It was grouped

into two. Part one focused on the findings from the questionnaire and the part two centered

on the findings established from the interviews conducted.

Chapter five focused on summary of findings, conclusion, and recommendations of the

researcher.

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CHAPTER TWO

LITERATURE REVIEW

2.0 Introduction

Chapter two reviewed previous research studies and publications conducted concerning the

effect of Public Procurement Act (PPA) on government finance policy at the procurement

unit, amidst of government trying to cut cost. This chapter contains overview of

procurement system; empirical literature, meaning of procurement, logistics management,

the Public Procurement Act, the Reform Program, Procurement Cycle, Procurement

Objective, the five right under PPA and the conceptual framework.

It also covered the sampled opinions of peoples work on the procurement of goods and

services in different categories.

2.1 Empirical Literature

According to a research carried out by Anvuur A. et. al (2006), it came out that there was no

comprehensive guidance on the scope and procedures of public construction procurement in

Ghana. The study identified five (5) pillars of the PPA (World Bank, 2003) of which when

followed will lead to the achievement of Value for Money, Transparency, Accountability,

Cost Reduction and elimination of Bureaucratic corruption. The 5 pillars identified are; (1)

Comprehensive, Transparent Legal and Institutional framework; (2) clear and standardized

procurement procedures and standard tender documents; (3) independent control system; (4)

proficient procurement staff; and (5) anti-corruption measures. The procurement and

construction have all along been controlled through issuance circulars emanating from the

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Ministry of Finance which brought together a series of instruction centered on adoptions

with reference to the exercising laid down rules on procurement by the Ministry Finance.

However, executers of World Bank projects dwelled on the rules emanated from the World

Bank with reference to procurement (World Bank, 1995) and advisors rules of the World

Bank (World Bank, 1997). The procurement rules adopted on public works was existed

traditional guidelines pertaining to construction. Guidelines were compulsory made

available towards the classification and registration of contractors into construction

controlled by the Ministry of Works and Housing. It was mandatory for MMDAs have

different steps contractors who are pre-qualified by use of special rules and regulations for

works on procurement.

The categorization of the Ministry of Works and Housing was seen as outmoded rules of

registration steps and the number of required contractors supported by the required financial

base checked at regular interval (Eyiah and Cook (2003) and World Bank (1996).

With reference to World Bank (1996), the treaming- down of contractors and consultancy

services have been seen over the years to be a repetition of same chosen civil engineers and

works superintendents (World Bank, 1996).

There are multiple of registered names or firms to execute projects that was surely won by

the same persons (Crown Agents (1998) and Westring (1997)

It was established that execution of construction works was badly done in Ghana coupled

with a lot complaints and reports that castigated lack of commercial stints of public sector of

execution of public procurement functions. Consultancy advice was delayed without any

justification prolonging project execution. (Crown Agent (1998) and Westring (1997).

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The courses of project delay execution coupled with negotiations after the award of works

and services led to the halt of expert opinion and recommendations, hindrance in project

evaluation, and multiples of checks, review and acceptance and tittles to property leading to

injunctions on constructions it came to light that execution of construction works and

services in Ghana led to project variations by contractors which led to some times poorly

done works by contractor of various projects, (Crown Agent (1998), Westring (1997) and

World Bank (1996, 2003).

It was established that contractors and suppliers are delayed in being paid, due to

unnecessary processes starting from issuance of invoices to receipt collection and writing of

cheque which were over delayed due to bureaucratic processes, thus coming out with delay

of payment to contractors upon execution of projects, works and services. (Eyiah and Cook

(2003), Westring (1997) and (World Bank (2003).

According to World Bank (1998), government excessive implementation of policies to

control the economy and badly managed procurement activities stated fro the unset brought

insecurity of funding of construction works and provision of unexplained reasons for

delayed payment of contract sum and arrears to contractors and consultants. It was

established that the unnecessary delay of contractors by MMDAs led to court actions to

claim interest due to delayed payment and intermittent changes of prices based on long

agreement to reach consensus leading to funding difficulties. World Bank (2003).

In the end, it was established that, contractors and consultants struggles a lot to complete

their works and be able to lay hand on their claims emanating from frequent increases in

prices (World Bank (1996). Moreover, various private contractor who provide services to

government tried unabated to cut cost by preventing losses through manipulations and

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abandoning projects in the mid- way has negatively impacted on execution of project which

brought bad relationship between contractors and their beneficiaries. Westring (1997).

Lastly, a number of MMDAs are obliged to make advance contractual payment way ahead

the expected period in other to overcome lapses made in the allocation of budget, payment

of mobilization to contractors can go beyond acceptable rate of 15% all in attempt to prevent

the work being abandoned. (Westring (1997) and World Bank (1996)

A research work done by Dowling (2011) ʽ Healthcare Supply chains in Developing

Countries’- Situational Analysis of Low and Middle income Countries. In this instance, it

was realized by the researcher that, due to lack of capacity in terms of infrastructure led to

insufficient storage facilities of logistics (drugs) in developing countries coupled with

limited capacity and often poor storage conditions.

2.2 Meaning of Procurement

Procurement is the over heading functions that describe the activities and process to acquire

goods and services. Importantly it differentiates from “purchasing” fundamental

requirements, sourcing activities such as market research and vendor evaluation and

negotiation of contract. It can also be purchasing activities required to order and receive

goods.

2.3 Logistics Management

According to Wikipedia.org, logistics management is defined as the part of supply chain

management that plans, implement, and controls the efficient, effective forward, and reverse

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flow and storage of goods, services, and related information between the point of origin and

the point of consumption in order to meet customers’ requirements.

According to Gyimah et al (2009), the process of managing efficient, effective and concise

logistics with reference to drugs, is the adoption of management information systems,

establishment of the needed funding, putting in place the channel of distributions and the

operations of the supply chain and improving, predicting and strategically establishing

procurement laid down procedures to be executed on works and services. Bossert et al

(2000) concluded that, Management of Information Systems (LMIS) on logistics are

recognized as very important weapon by the experts minus the basic and needed systems of

logistics for the very important information needed effectively and efficiently manage

procurement of construction works. United State Agency for International Development,

USAID/DELIVER (2011) echoed the effective and efficient manage and use of non-drugs

consumables and drugs by dwelling on appropriate delivery of goods and services needed by

health institutions needed for their day to day activities to save human life coupled with

provision of affordable healthcare to all. Provision of basic essential health commodities

(non- drugs consumables and drugs) is the establishment of security in logistics provision.

Poulin (2007) queried the assistance offered through processes in light of the management of

health commodities being the main target when it comes to healthcare delivery and practices

towards empowering regional, district hospitals and district health administrations in their

operations for effectiveness and efficiency.

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2.4 Public Procurement Act (PPA)

The Government of Ghana in the year 2003, enacted and accented the Public Procurement

Act 2003,(Act 663) into law to govern the procurement of Metropolitan, Municipal,

Department and Agencies (MMDAs) and all other state funded agencies in their activities to

conform to rules and regulations of the Act in August, 2004.

2.5 The Reform Program

The overall changes to the Public Procurement Act are a wider activity planned to achieve or

come out with smooth and efficient management of public funds. The objective of the

various reforms of the PPA is to encourage total national development, to bridge both local

and international rules systems and regulations, encourage healthy competition, genuine

business dealings, accountability, reliability and transparency, to bring about better

procurement management to ascertain value for money. Ministry of Finance (2001);

proposed an annual estimation of US $150million through proper management of

procurement activities leading to cost. (World Bank, 2003). The introduction of government

funding ceiling on public expenditure to MMDAs with strict adherence from the budgeted

figures based on cash flow analysis and internal controls to check abuse of government

funding and prosecution of people culpable in addition to standards of procedures to follow

by holders of public offices. Purchase of goods and services must come with approval from

the ministry of finance indicating funding availability prior to award of any contract or what

so ever.

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2.6 Procurement Cycle

According to the Public Procurement Act 2003(Act, 663) the underlining steps must be

adhered to when it comes to procurement of goods and services towards achieving fairness,

cost reduction, best offer, value for money.

THE PROCUREMENT CYCLE

Figure 1.

2.6.1 Planning

Planning of procurement brings about the decision as to what to buy in terms of procurement

activities, when to buy it and from what funding.

During planning, procurement procedures and methodology are determined by the

requirement of procurement expectations in fulfillment of procurement outcome. Planning

of procurement plays a crucial role below

PLANNING

SOURCING

CONTRACTING

CONTRACT MANAGEMENT

STORAGE

DISTRIBUTION

DISPOSAL

EVALUATION

Source: PPA Act, 663 (2003)

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First and fore most, it helps improve on buyer’s expectations and realistic outcomes.

Secondly, it encourages the involvement of management processes by managers to come

together to elaborate on procurement requirement. Thirdly, it gives way for procurement

strategies to be established for procurement plans that would be factored into the

procurement requirements. Lastly, stakeholders can lay bare procurement processes and

requirement for the award of contract and the time expected to execute the contract.

2.6.2 Sourcing

The procurement steps are reached at this level. The expected outcome at this stage in

procurement activities is sieving of potential supplier, issuance of tender document,

responses evaluated and the choosing tenderers who are successful.

Contracting

The acceptable steps are factored into awarding of contract to a start of any contract and

agreement terms reached by parties into the contract. The underlining principles is the order

purchase.

Contract Management

Regulation of contract management is the exercising of responsibilities attached to handling

of contracts such evaluation of bids, award of contract, implementation of contract, steps of

completed works and calculation of payments to be made. It also involves monitoring

contract relationship, addressing related problems incorporating necessary changes and

modifications in the contracts to ensure that both parties meet or exceed each other’s

expectations.

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2.6.3 Storage

In procurement, storage or stores is where logistics or accoutrements that are not being used

immediately are stored, making sure that no damage or loss happens. The value of stock can

be high and timely availability can be crucial to organization’s operations. Logistics may

require particular storage conditions or have limited shelf life, so effective storage, handling

and management of stock levels are important

.2.6.4 Evaluation

Procurement processes and efficient purchasing function is the end product of performance

control to have efficient, effectiveness to management of procurement. It is of importance to

look at procurement processes and evaluation done to establish comport level, come out

with weaknesses and draw a strategy to prevent it from occurring in the future. PPA Act 663

(2003).

2.6.5 Distributions

Goods in storage need to be delivered to their final destination in accordance with customer

requirement. Distributions may involve issuing of the drugs from store to the main

dispensing area for onward sale to the client or patient.

2.6.6 Disposal

Public tendering is used to dispose of obsolete, unserviceable, and surplus stocks based on

the condition and nature of the goods involved. The unserviceable items may be disposed off

through an auctioneer or deploying to another organization or through public tender or

destroying the items in question and finally to adjust the prices down to meet the value of

the goods disposed and any income realized must be accounted for. PPA 2003 (Act 663).

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2.7 Procurement Objectives

The bottom-line of the introduction of Procurement into the daily activities of the nation is

importance and to serve specific objectives and the strict adherence of it leads to

achievement of value for money.

2.7.1 Professionalism

With the training of procurement professionals at our tertiary institutions followed by the

recruitment, plays a vital role in the rendering of procurement services towards the

development of the Ghanaian economy. PPA 2003(Act 663).

2.7.2 Transparency

The existing rules being the five pillars of procurement are applied to have transparency in

procurement dealings. Fellow competitors should not have an edge over other competitors

based on leaked inside dealings. There should be fairness, openness and transparency to all

competitors and this will go a long way to enhance procurement activities devoid of

suspicion.

2.7.3 Value for Money (VFM)

Value for money in the Ghanaian context bothers on the lowest bidder. However being the

lowest bidder does not equate corresponding good jobs and services but the crucial yardstick

is the measure of the effectiveness of the procurement procedures depending on the output

and the outcome but also considers quality of goods, availability of resource, the cost

involve and its usefulness for the destined purpose, delivery on time and to establish the

convenience to judge when put the under mentioned together will lead to value for money.

To come out with value for money, there is the need for strategic planning to have that.

17

2.7.4 Competitiveness

Potential bidders must be given equal opportunity of platform to bid for tender documents

when it comes to award of contract. There should be fair competition coupled with required

documentation and openness of competitive procurement activities to bring about;

 Potential for cost savings

 Increase the potential supplier base

 Greater awareness of new development

 Greater understanding of the Act and the confidence in the public sector

2.7.5 Accountability

Is an attempt to hold individuals and organizations who are involved in procurement

activities to render vivid accounts of stewardship of which they have authority to do. PPA

Act 2003(Act, 663)

The merits of being accountability leads to change of perception about transparency and

fairness. These zeros down corruption.

2.7.6 Fairness

There should be impartiality towards all bidders when it comes to competing for goods,

services and works by giving everyone the equal opportunity to win the contract provided all

necessary procurement are met.

2.7.7 Efficiency

This is timely manner in which procurement activities are executed devoid of personal

issues and bureaucracy.

18

2.8 The Five Rights Under Public Procurement Act

The supplier and the purchaser must adhere to the right under the procurement Act 2003

(Act 663).

2.8.1 The right quality

The right quality seen by the consumer is based on a prototype normally given to check its

efficacy to human consumption, fitness of purpose, waste elimination and continuous

improvement.

2.8.2 The right quantity

It is the measurement of the appropriate expected quantities out of a contract and the

requirement of the parties involved. Prices can be negotiated down for voluminous of goods

and this may come in conflict with storage, production, and capabilities.

2.8.3 The right time (To buy and to deliver)

The following factors; availability of product, the market state (competition) and policies on

procurement, influences the right time to procure. Re- order level brings about when to

deliver goods and services based on the needs of the customer.

The right place

Procuring from the authentic source is a guarantee of quality goods and services which leads

to a guarantee of efficient and effective procurement

19

2.8.5 The right price

There should be a window shopping or soliciting of prices from various quarters of product

specification to give room for price comparism and subsequent price bargain to achieve

value for money.

2.8 Notification of Unsuccessful Tenderers

Notices with fruitful or reasonable explanation must be sent to or be posted to bidders who

were not successful in their attempt secure contract of goods or services after provision and

going through all the necessary procedures to be selected. It informs the unsuccessful

tenderer why he lost his bid and prevents legal action to be instituted upon failure by the

public procurement board to inform him PPA 2003 (Act 663).

2.9 Controlling and Surpervising the Procurement Cycle

The various steps in achieving value for money are the strict adherence to the various steps

that are needed to be respected in terms of procurement from the beginning to the end.

Management and other members who form the procurement team should educated on the

various steps available for their strict compliance which involves planning the procurement,

preparation of bid document, issuing of tenderers and tender evaluation, awarding and

management of the contract.

20

2.10 Ensuring quality of the Product

Project management team should be put in order to monitor project specifications and the

strict adherence by the executor of the project from the beginning to the end. Steps must be

taken to ensure that quality product to the receiver from the supplier, meet the standers set in

the contract document to execute the project in question (WHO 1999). Under listed are the

four procedures to have quality assurance;

1. The picking of dependable suppliers of drugs and pharmaceutical consumables, 2.

Adoption of a strategy or mechanism of quality assurance and authentication procedure of

WHO towards achieving pharmaceutical products (WHO 1996). 3. Institution of systems to

check expired drugs and products are defective and the reporting lines to uncover tem. 4.

Institution of mechanisms to check on and after transporting test. More significantly,

contractors of good track records are encouraged to most at the times awarded contracts for

provision of quality of service

More importantly suppliers that have record of providing very high-quality product in the

previous procurement is vital to ensuring the receipt of quality goods.

2.11 The Outcome of Effective Procurement System

To efficiently and effectively management a sound procurement system, the institution

concerned must adhere to legislation rules and regulations in their supervision. The under

listed must be adhered to achieve effective procurement system.

21

Compliance with government policy and legislation: To enhance to usage of public funds

having in mind to achieving value for money, MMDAs per by virtue of being under that

state must adhere to all the rules both locally and internationally to acquires and services.

They should also follow the institution of good and sound provision of healthcare as a

whole.

Enhancing logistics and service delivery: Government did not come by rules and

regulations governing procurement activities out of the blue. It was specifically brought up

to do away with unnecessary shortage of goods and services when it emanate from MMDAs

to put the government of the day in bad state of its citizenry. For effective procurement, the

onus lies on the procuring and the managing entity to have the right thing done.

Addition of value to logistics and service management: Crucial processes to honor or to

acquire procurement of goods and services indicate that the public procurement processes

adhere to their rules and regulations which must be linked to accountable to ascertain value

for money.

22

2.12 Conceptual Framework

Figure 2 Conceptual Framework

Source: Researcher’s construction, 2015

The Procurement Processes (PPA) (Act 663, 2003) which helps towards achieving value for

money starts from identification of needs followed by procurement procedures, which

enhances procurement of logistics leading to identification of suppliers of the needed

logistics. Request for quotation and prices are made which culminates into the selection of

supplier with the right product coupled with the economized price. However, an assessment

of a prototype is made and upon satisfaction, final acquisitions of the good(s) are made and

sent into stores for safe – storage. These are served to clients upon request made by the

healthcare provider

Achieve

Value for

money

Procureme

nt

procedures

Identification of needs

Identifica

tion of

suppliers

Request

for

quotation

& pricing

Selection of

suppliers

with the

right pdt&

Assessm

ent of a

product

Final

selection

Storage

23

CHAPTER THREE

RESEARCH METHODOLOGY

3.1 Introduction

This chapter focuses on the research design, target population, sample and sampling

procedures, data collection methods, analysis of data, scientific credibility, limitation of the

study and ethical consideration. It also discusses the profile of the study area.

3.2 Research Design

Research design is a detailed outline of how an investigation took place. It is the framework

that has been created to seek answers to research questions. They are sometimes used to

challenge traditionally held scientific theories. It leads to new theories about psychological

phenomena either through pilot research or through case study on their own and allows

researchers to study the sample relationship between phenomena, context and people. The

following research designs were used in the research, namely; case study, quantitative,

qualitative and descriptive studies.

Case study is an approach to research that focuses on gaining in-depth guidelines for

designing and conducting research. As Willing (2008) asserts, case studies “are not

characterized by the methods used to collect and analyze data, but rather, it focus on a

particular unit of analysis”.in this research, SGH was used as a case study to obtain in-depth

knowledge of assessing PPA in the management of drugs because it was bounded in

examining a specific set of individuals to obtain result of the research. However,

questionnaires were administered to the individual respondents to ascertain their opinions or

24

inputs to help take a decision which came to support quantitative aspect of the research.

Furthermore, results of the interview conducted on four managers were also analyzed and

the results put together with that of the questionnaires to come with the various findings

obtained. Once again, the reason for the use of this methodology helped the researcher to

obtain the necessary data and the corresponding outcome of the research.

Quantitative research is a more logical and data- led approach which provides a measure of

what people think from a statistical and numerical point of view. It is about asking people

for their opinion in a structured way so you can produce hard facts and statistics to guide

you to get reliable statistical results. The researcher applied a mixed method involving the

quantitative as well as the qualitative approaches. At SGH, the population was stratified and

simple random and purposive sampling methods were used to select the respondents.

Qualitative research is a method of enquiry employed in many different academic

disciplines, traditionally in social sciences. It is about exploring issues, understanding

phenomena and answering questions by analyzing and making sense of unstructured data.

Qualitative research was used when 4 managers were interviewed to solicit their responses

to draw a conclusion on the research.

The study is descriptive in the sense that information is collected without changing the

environment (i.e. nothing is turned around). Descriptive study can involve a onetime

interaction with a group of people (cross sectional studies) or a study might follow

individuals over time (longitudinal studies). To do this the researcher interacted with

participants through interview to collect the necessary information.

25

3.3 Population

The entire staff of SGH formed the population of the study. This comprised staff in the

following departments; Records, Statistics, Procurement, Public Health, Nursing, Medical

Doctors, Accounts, Family Planning, Eye, General OPD, Pharmacy, X-Ray and Laboratory.

3.4 Sample and Sampling Procedures

Sixty- three (63) respondents were chosen out of the total population. The sampling

techniques applied were simple random, stratified sampling and purposive sampling. The

choice of the sampling technique depends on the research questions and objectives.

Stratified sampling is a method of sampling from a population. In statistical surveys, when

sub populations within an overall population vary, it is advantageous to sample each sub

population independently. A stratified sample is made up of different ‘layers’ of the

population, for example; selecting sample from different age groups. The advantages of

using stratified sampling in a research are that, it makes measurement becomes more

manageable and / or cheaper when population is grouped into strata’s and it often desirable

to have estimates of population parameters for groups within the population.At Suhum

Government Hospital stratified sampling was used to select the various respondents from the

various units or strata namely, Records, Statistics, Procurement, Public Health, Nursing,

Medical Doctors, Accounts, Family Planning, Eye, General OPD, Pharmacy, X-Ray,

Laboratory and questionnaires were administered to the respondents for the solicitation of

raw data to serve as a guide for the researcher.

Random sampling is a method in which individuals are chosen from a group randomly and

entirely by chance, such that, each individual has the same probability of being chosen at

26

any stage during the sampling process. Simple random sampling was used at SGH to select

the respondents after the total population had been stratified into groups shown in the table

below

Unit Number of respondents

Records 1

Statistic 1

Procurement 3

Public health 2

Nursing 23

Medical doctors 4

Accounts 5

Family planning 2

Eye 2

General OPD 5

Pharmacy 8

X- Ray 2

Lab 5

Total 63

Source: Researcher’s work

Purposive sampling relies on the judgment of the researcher when it comes to selecting the

unit (e.g. people, cases/ organizations, events, pieces of data) that are to be studied. Usually,

the sample being investigated is quite small especially when compared with probability

27

sampling techniques. The main goal of purposive sampling is to focus on particular

characteristics of a population that are of interest, which will be best to enable you to answer

your research questions. In this regard, the researcher selected four managers, namely the

Medical Superintendent, the Health Service Administrator, the Pharmacist, and the

Procurement Officer for the purpose of conducting interview under the various sub-

headings emanated from the research objectives and the results analyzed to aid findings.

3.5 Data and data collection procedures

Two types of data were used for the research. These were primary and secondary data.

Primary data is that which is collected by sociologists themselves during their own research

using research tools such as experiment, survey questionnaires, interviews and observation.

Primary data is the best source of information and always good for surveys. It goes with

various advantages; original data, unbiased information, data from primary market/

population, basic data and data direct from the population. Questionnaires and interviews

were mainly used to obtain the primary data or information for analysis from the

respondents. These instruments helped the researcher to have the needed information to

complete and make a good analysis of the research.

28

The diagram below depicts primary data collection steps.

Source: Ghauri and Gronhaug (2005). Reviewed; Chen Y. et. al (2011)

The most perfect process to generate input from the respondents is the usage of interviews

and questionnaires. Qualitative interview comes out with important knowledge of

communications inputs. Respondents are expected to state their feelings or opinions without

fear of favor. (Lindlof,1995). Various ways to conduct interviews include; structured

interview and unstructured interview. Structured interview method was used for the research

work. At SGH questionaires interviews were used to obtain the data from the respondent to

form the basis for the primary data for the research studies.

The researcher did some pilot studies. Polit and Hungler, (2003), see pilot testing as a

miniature version or trial done prior to the actual study. The reason for conducting pilot or

pre- testing study is to ensure an appropriate level of accuracy and dependability of the data

collection instruments. Pilot testing further helps the researcher to go straight to what his

demands are.

One of the merits of organizing pilot or pre- testing is that, it might indicate where the main

research could fail way ahead of time. It may also indicate where research protocols may not

be followed or whether the suggested procedures or instruments are not in order or too

Primary data

experiment Observations

(human/ mechanical) Communication

(survey/interview)

29

cumbersome. According to De Vaus, (1993), pre- testing is essential for the under listed

reasons;

Firstly identifying logistical problems, this might crop up upon the usage of the proposed

methods. Secondly, coming out with the needed resources for the planned research (finance,

staff).

Thirdly, developing and organizing accuracy testing of the instruments to be used for the

research.

Lastly, forecasting variability in establishing the outcome to offer assistance in establishing

the sample size.

The research instruments were piloted at New Abirem Government Hospital in the Eastern

Region of Ghana. Ten (10) respondents were chosen from the various units randomly using

simple random sampling techniques (lottery method) to represent the total sample size.

Secondary data is the data that have been already collected and readily available from other

sources. Such data are cheaper and more quickly obtainable than primary data and also may

be available when primary data cannot be obtained at all.

30

The diagram below depicts secondary data collection steps;

Source: Ghauri and Gronhang, (2005). Reviewed; Chen Y. et. al (2011)

Secondary data used in this research was picked from internal and external sources. Inputs

and reports taken from the procurement department formed part of the thesis write – up.

For the purpose of this research, other peoples work, journals and manuals on procurement

and management of drugs were read through and very vital information picked and the

source credited. These were used in addition to the primary data to draw conclusions to

confirm or not, previous conclusions by other researchers and recommendations made upon

the outcome that could also be used by future researchers for the same indicators.

3.6 Scientific credibility

With time credibility is ascertained when it comes to in both agreed and precise ways. It

indicates our coexistence with friends and supporters. In principle, it is about the genuine

and sincere evaluation of other people’s work and credit accorded accordingly. It is about

Secondary data

External source Internal source

Published (books &

articles, statistics,

research repot etc.

Commercial ( panel

research monitors etc.)

Reports from different departments ,

invoices, brochures’ and catalogues

etc.

31

documentation, storage of our final findings vis-à-vis the transparency and special attention

in the experiment reporting. In cases of inability to showcase credibility, our work will not

be built upon by others of which the movement of scientific development is put on hold. The

genuineness of an empirical outcome of research would be sincerely based on credibility,

dependability and trustworthiness. Yin, (2008).

3.7 Analysis of data:

It is the process of inspecting, cleaning, transforming and modeling data with the goal of

discovering useful information, suggesting conclusion and supporting decision making. Data

generated from the respondents of the various units within the hospital was processed with

excel spread- sheet to come out with tables, percentages and bar charts, and these aided the

discussion and interpretation leading to conclusions on the objectives of the research.

3.8 Ethical consideration:

The chance to conduct the thesis research was given a green light by the Medical

Superintendent of Suhum Government Hospital and his management team of which a

promise was made to keep all information strictly private and confidential. This led to total

cooperation of all employees of the hospital without obstacles.

32

3.9 Profile of Suhum Government Hospital

The Suhum Government Hospital was started as a Health center in 1958 and was upgraded

to a District Hospital in 1959. The hospital is located in the Suhum Municipality and is

found on the main highway between East Akyem Municipality and Kraboa Coaltar District.

The hospital serves a catchment population of 87,514 (2010, population census) and has a

bed capacity of 131 with a work force of 250.

Vision

The vision of the Suhum Government Hospital is to become a center of excellence in the

provision of quality, affordable and accessible health care to all people living within the

catchment area.

Mission

Suhum Government Hospital is committed to the delivery of health care through client

focused activities, with a well-trained, motivated, disciplined and result -oriented staff.

Overview of activities

The Suhum Government Hospital offers the under listed range of services: Eye services,

Outpatient care, Inpatient care, Pharmacy, X-ray, Laboratory, Public health and Family

planning among others.

33

CHAPTER FOUR

RESLUTS AND DISCUSSIONS

4.1 Introduction

This chapter indicates the analysis of data obtained from the 63 respondents which the

researcher took from the study area to address the objectives of the study.

4.2 Background Attributes of Respondents in Percentages

Figure. 3: Gender of the Respondents

Source: Field Data June 2015.

Figure 3 depicts that, out of the 63 interviewees, 54% were males and 46% were females.

The figure indicates, that the male workers out numbered the female workers of the Suhum

Government Hospital.

42

44

46

48

50

52

54

56

MALE FEMALE

PERCENTAGE

PERCENTAGE

34

4.3 Department of Respondents

Table 1:

DELPARTMENT FREQUENCY PERCENTAGE %

RECORDS 1 1.59

STATISTICS 1 1.59

PROCUREMENT 3 4.76

PUBLIC HEALTH 2 3.17

NURSING 23 36.51

MEDICAL DOCTORS 4 6.35

ACCOUNTS 5 7.94

FAMILY PLANNING 2 3.17

EYE DEPARTMENT 2 3.17

GENERAL OPD 5 7.94

PHARMACY 8 12.70

X- RAY 2 3.17

LABORATORY 5 7.94

TOTAL 63 100

Source: Field Data June 2015

From table 1, the nurse’s grade formed the highest respondent group with 36.51%, followed

by the pharmacy with 12.70%. Accounts, general OPD and laboratory were next after the

pharmacy. Medical doctors, Procurement officers, X- ray staff, Eye unit, Public health,

35

Statistics, Family planning and Medical records on their part formed 6.35%, 4.76%, 3.17%,

3.17%, 3.17%, 3.17%, 1.59% and 1.59% respectively.

4.4 Academic Qualification of Respondents

Table 2

QUALIFICATION FREQUENCY PERCENTAGE %

SENIOR HIGH 11 17.46

DIPLOMA 34 53.97

FIRST DEGREE 12 19.05

SECOND DEGREE 6 9.52

TOTAL 63 100

Source: Field Data June 2015

Table 2 indicates the educational qualification levels of the respondents. Majority of the

respondents had diploma followed by first degree holders with a percentage of 19.05%.

Following was Senior High School education with a percentage of 17.46% and lastly

holders of Second Degree with a percentage of 9.52%. It can be established that, the chunk

of staff of Suhum Government Hospital have a very good academic qualifications which

will affect their final output positively. Below is a pie chart of the academic qualification of

the respondents as explained in table 2 above.

36

4.5 Existence of the Procurement Act (PPA)

Table 3

VARIABLE FREQUENCY PERCENTAGE %

YES 63 100

NO 0 0.00

TOTAL 63 100

Source: Field Data June 2015

In response to the question whether respondents know about the existence of the PPA, 100%

indicated, that they knew about the existence of the Public Procurement Act 663, (2003).

This indicates that all the respondents were aware of the existence of the Public Procurement

Act.

17%

54%

19%

10%

Accademic Qualification

SENIOR HIGH

DIPLOMA

FIRST DEGREE

SECOND DEGREE

37

The pie chart above further explains the tabular presentation of the existence of the

procurement Act.

4.6 How Respondents got to know about the Public Procurement Act

As to how respondents got to know about the PPA, it was indicated from table 4 below that,

42.86% heard about the PPA during training and staff durbars. Whilst 28.5% knew the

existence of the public procurement Act through the media, 15.87% got to know it during

management meetings and 12.70% through friends. This indicates that the majority of the

staff at Suhum Government Hospital have heard about the Public Procurement Act 2003

(Act 663).

YES 100%

Existence of Procurement Act

38

Table 4

VARIABLE FREQUENCY PERCENTAGE %

IN THE MEDIA 18 28.57

THROUGH FRIENDS 8 12.70

DURING TRAINING/

STAFF DURBAR

27 42.86

AT MANAGEMENT

MEETING

10 15.87

TOTAL 63 100

Source: Field Data June 2015

In the next page is a pie chart pictorial view of how respondents got to know about the PPA

at SGH.

28%

13% 43%

16%

How respondents heard about the PPa Act.

IN THE MEDIA

THROUGH FRIENDS

DURING TRAINING/ STAFF DURBAR

AT MANAGEMENT MEETING

39

4.7 What the Public Procurement Act, 2003 (Act 663) Stands For.

To ascertain from respondents what the PPA stood for, it was established from table 5 that,

42.86% described it as an Act of parliament while sixteen (16) respondents, representing

31.75%, described it as a Procurement guideline. Moreover, 15.87% of the respondents

described it as tender documents and 9.52% of the respondents knew it to be just a mere

policy document. It can be realized that, many of the respondents at Suhum Government

Hospital knew the Public Procurement Act 2003 (Act 663) as an Act of parliament.

Table 5

VARIABLES FREQUENCY PERCENTAGE %

A MERE POLICY

DOCUMENT

6 9.52

AN ACT OF PALIAMENT 27 42.86

PROCUREMENT

GUIDELINES

20 31.75

TENDER DOCUMENT 10 15.87

TOTAL 63 100

Source: Field Data June 2015

40

4.8 Access to a Copy of the Public Procurement Act?

Table 6

VARIABLE FREQUENCY PERCENTAGE %

YES 13 20.63

NO 50 79.37

TOTAL 63 100

Source: Field Data June 2015

To know whether the respondents have had access to copies of the PPA, it was realized as

shown in table 6 that, 79.37% have not had access to a copy of the Public Procurement Act.

However, 20.63% of the respondents indicated otherwise. It can be deduced from the result

that majority of the respondents have not had access to a copy of the Public Procurement

Act. The pie in the next page further enhances table 6 above.

No 79%

Yes 21%

Access to a copy of the PPA

41

4.9 Reading Through or Making Reference to the Public Procurement Act?

Table 7

VARIABLE FREQUENCY PERCENTAGE %

YES 11 17.46

NO 52 82.54

TOTAL 63 100

Source: Field Data June 2015

To ascertain whether the respondents have read through or made reference to the PPA, it

came out clearly as indicated in table 7 that 82.54% have never read through or made

reference to the Public Procurement Act, while 17.46% have read through and also made

references to the Public Procurement Act. It can be realized that the majority of the

respondents have not read through the Public Procurement Act 2003 (Act 663).

COMPLIANCE WITH PPA WITH REGARDS TO STORES PROCEDURE

The researcher through interview solicited the views of management members, namely; the

Medical Superintendent, the Health Service Administrator, the Pharmacist and the

Procurement Officer, to find out whether Suhum Government Hospital complies with the

provisions of the PPA in its stores procedures.

Information received from all the four officers indicated that the hospital has a well-

established store unit where goods bought are kept for onward distribution to the various

units that need them.

As to whether Suhum Government Hospital has qualified stores personnel, the four(4)

managers of the hospital interviewed, came out clearly that, the hospital has qualified stores

42

personnel who have the requisite qualification coupled with in-depth stores management

skills to man the hospital’s stores unit.

As to the educational background of the store keeper of the hospital, the 4 officials

interviewed clearly stated that, he holds a Higher National Diploma (HND) in purchasing

and supply and that, he is very capable to handle the activities of the stores especially in

respect of receipts and issues of drugs and proper documentation of stores transactions. They

supported this claim by the fact that, auditors occasionally examine the stores management

of the Suhum Government Hospital and give a very good report.

It was realized from the interview that, the stores personnel holds the rank of a supply

officer which is, equivalent to the Higher National Diploma in Purchasing and Supply and a

Senior Officer grade in the Ghana Health Service. This indicates that the majority of the

Health Management Team (HMT) of Suhum Government Hospital knew about the rank of

the store keeper.

It was realized from the interview that, three officers work at the hospitals stores unit in

addition to the storekeeper also known as supply officer. The supply officer among other

duties oversees the day- to- day running of the store in addition to maintaining the

cleanliness of the stores and its environs and also ensuring proper arrangement of items in

the store for easy retrieval.

It came out clearly from the interview that, the hospital has no spacious storage facilities to

keep items due to lack of building infrastructure. It was further realized from the interaction

that, they are unable to put up a spacious building to be used as stores due to the suspension

43

of capital expenditure by government and the inability of the hospital to raise sufficient

internally generated fund (IGF) for the purpose.

The officers interviewed said that all items were listed in the stock register as well as stores

receipt voucher before they are received into the stores and issued out of the store upon

demand, indicating that there was a procedure for accepting drugs into stores and issuing

drugs out of the store.

It was established as a fact that, the hospitals internal auditor does checking of each and

every item received or issued out of the store and this is done by way of the certification of

all requisition books from various units of the hospital to the stores for items. The officers

interviewed further stated that the internal auditor does on the spot check of the hospital

stores once every week with the stores keeper and issues a report to management for

consideration.

All the interviewees disagreed that the poor procurement organization and procedures led to

employees of the Suhum Government Hospital not complying with the PPA.

The interviewees strongly agreed that, the PPA is an act passed by parliament and all

departments and agencies are supposed to comply and that under no circumstance will one

attribute lack of qualified procurement staff as a basis for non-compliance.

The interviewees disagreed that, poor stock management prevented staff of Suhum

Government Hospital from complying the Public Procurement Act 2003 (Act 663).

44

THE EFFECT OF THE IMPLIMENTATION OF THE PPA ON THE

PERFORMANCE OF THE HOSPITAL

4.10 What does the Public Procurement Act Seek to Achieve?

Table 8

VARABLE FREQUENCY PERCENTAGE %

RIGHT PROCUREMENT

PROCEDURES

29 46.03

JUDICIOUS USE OF

GOVERNMENT FUNDS

21 33.33

EQUITY AND FAIRNESS 13 20.64

TOTAL 63 100

Source: Field data June 2015

To solicit from respondents what the PPA seeks to achieve, the result in table 8 above shows

that, 46.03% are of the opinion that, the Public Procurement Act seeks to achieve right

procurement procedures, while 33.33% indicated that, the Public Procurement Act seek to

achieve the judicious use of government funds. 20.64% thought the Public Procurement Act

seeks to achieve equity and fairness. It can therefore be established that, many of the

respondents were of the opinion that the Public Procurement Act defined the right

procurement procedure. Furthermore to boost table 8 above is the pie chart below.

45

4.11 The PPA is Useful in the Conduct of Business of the Hospital

Table 9

VARIABLE FREQUENCY PERCENTAGE %

HIGHY AGREE 44 69.84

AGREE 10 15.87

DISAGREE 4 6.35

HIGHLY DISAGREE 5 7.94

TOTAL 63 100

Source: Field Data June 2015

To establish whether the PPA is useful in the conduct of business of the hospital; as shown

in table 9, 69.84% highly agreed that it is useful, while 15.87% agree that it is useful.

14.92% of the respondents disagreed that it is useful. It was realized that the majority of

46%

33%

21%

What does the PPA seek to achieve?

RIGHT PROCUREMENT PROCEDURES

JUDICIOUS USE OF GOVERNMENT FUNDS

EQUITY AND FAIRNESS

46

respondents agreed that, the application of the Public Procurement Act at Suhum

Government Hospital was useful.

As to the question whether the implementation of PPA has resulted in value for money,

Table 10(a) and the pie chart below, indicates that 61.91% of the respondents who were in

the majority highly agreed that, the Public Procurement Act has achieved value for money.

Table 10 (A) Value For Money;

VARIABLE FREQUENCY PERENTAGE %

HIGHYLY AGREED 39 61.91

AGREED 18 28.57

DISAGREED 6 9.52

HIGHLY DISAGREED - -

TOTAL 63 100

Source: Field data June 2015

62%

29%

9%

0%

Value for money

HIGHYLY AGREED

AGREED

DISAGREED

HIGHLY DISAGREED

47

Table 10(B) Accountability

VARIABLE FREQUENCY PERCENTAGE %

TO A LARGE EXTENT 27 42.86

TO SOME EXTENT 30 47.62

TO LITTLE EXTENT 6 9.52

TO NO EXTENT - -

TOTAL 63 100

Source: Field Data June 2015

Table 10(b) shows that, 42.86% of the respondents were of the opinion that to a large extent

the Public Procurement Act brought about accountability while 47.62% agreed with the

assertion to some extent; inferring that the implementation of the Public Procurement Act

brought about accountability. Below is a pie chart to highlight the presentation in table 10(b)

above.

43%

48%

9%

0%

Accountability

TO A LARGE EXTENT

TO SOME EXTENT

TO LITTLE EXTENT

TO NO EXTENT

48

Table 10 (C) Cost Reductions

VARIABLE FREQUENCY PERCENTAGE %

TO A LARGE EXTENT 18 28.57

TO SOME EXTENT 45 71.43

TO A LITTLE EXTENT - -

TO NO EXTENT - -

TOTAL 63 100

Source: Field data June 2015

Table 10 (c) indicates that 28.57% and 71.43% were of the opinion that the implementation

of the Public Procurement Act to a large and some extent respectively brought about cost

reduction. Below is a pie chart of table 10 (c).

29%

71%

0% 0%

Cost Reduction

TO A LARGE EXTENT

TO SOME EXTENT

TO A LITTLE EXTENT

TO NO EXTENT

49

Table 10 (D) Quality Of Service Delivery.

Table 10(d) and the pie chart below shows that, 21 and 30 of the respondents, representing

33.33% and 47.62% respectively agree to some extent that the Public Procurement Act has

led to quality service delivery.

VARIABLE FREQUENCY PERCENTAGE %

TO A LARGE EXTENT 21 33.33

TO SOME EXTENT 30 47.62

TO A LITTLE EXTENT 12 19.05

TO NO EXTENT - -

TOTAL 63 100

Source: Field Data June 2015

33%

48%

19%

0%

Quality of service delivery

TO A LARGE EXTENT

TO SOME EXTENT

TO A LITTLE EXTENT

TO NO EXTENT

50

Table 10 (E) Transparency

VARIABLE FREQUENCY PERCENTAGE %

TO A LARGE EXTENT 30 47.62

TO SOME EXTENT 18 28.57

TO A LITTLE EXTENT 15 23.81

TO NO EXTENT - -

TOTAL 63 100

Source: Field data June 2015

As to the extent to which PPA has affected transparency in the management of stores in the

hospital; table 10(e) and the pie chart shows that, 47.62% of the respondents concluded that

it has to a large extent brought about transparency while 28.57% indicated that it has to

some extent resulted in transparency. This shows that the implementation of the Public

Procurement Act 2003 (Act 663) brought about transparency.

48%

28%

24%

0%

Transparency

TO A LARGE EXTENT

TO SOME EXTENT

TO A LITTLE EXTENT

TO NO EXTENT

51

CHAPTER FIVE

SUMMARY OF FINDINGS, CONCLUSIONS AND RECOMMENDATIONS

This chapter discusses the summary of findings, conclusions and recommendations of the

research.

5.1 Summary of Findings

The following findings were established from the analysis of the result of the study;

It was established that, staff of SGH were aware of the existence of the Public Procurement

Act 2003(Act 663).

It was realized that, staff in SGH have heard about the Public Procurement Act 2003 (Act

663).

It was realized from the study that, Suhum Government Hospital considers the Public

Procurement Act 2003 (Act 663) as an Act of parliament.

From the study, it was clearly established that; staff of SGH have either not seen or got copy

of the Public Procurement Act 2003 (Act 663).

It came out explicitly, that the staff of SGH have not read through or made reference to the

Public Procurement Act in their endeavors.

It was established that the application of the Public Procurement Act is very useful at SGH.

It was established that, the Public Procurement Act seek to define the right of procurement.

52

It was highly agreed that the application of the PPA was useful in the conduct of the

business at Suhum Government Hospital.

It was established that, Suhum Government Hospital indeed had a store unit where drugs

procured are kept for safe keeping and for onwards transmission to the user unit upon

request.

It was also revealed that, Suhum Government Hospital had qualified stores personnel to

manage the day- to day activities of the store, which confirms Omari- Siaw B. (2014) work

on “Assessment of Pharmaceutical Supply Chain” done at Kwahu Government Hospital

(KGH), Atibie in the Eastern Region of Ghana. This also attest to the fact that personnel

involved in the procurement processes indeed had knowledge and requisite training in the

handling of procurement activities which goes contrary to Dowling, (2011) work on

“Healthcare Supply Chains in Developing Countries” which indicated that, in lower and

middle income countries, there is lack of skilled personnel with requisite training for them to

function appropriately.

It also came out that the stores personnel are highly qualified and have an in-depth

knowledge when it comes to delivery on their work and this was confirmed by Omari- Siaw.

B. (2014).

It was established that the rank of the stores personnel at SGH was known.

It established that three staffs work at the Suhum Government Hospital stores to facilitate

the smooth running of the stores.

53

It was realized that there wasn’t enough space at the stores where drugs are kept. This

confirms Dowling (2011)’s Analysis of Poor Supply Chain System compounded by

insufficient storage space in developing countries. It furthermore came to confirm Omari-

Siaw, B. (2014)’s research conducted at KGH, Atibie in the Eastern Region of Ghana.

It was established that the procedure for accepting drugs into stores is the listing of all items

in stores receipt vouchers, which confirms Dowling’s situational analysis on healthcare

supply chains in developing countries.

It was established that, the internal auditor does the checking of drugs before they are

received into stores.

It was established that the Public Procurement Act had to a large extent contributed to the

achievement of value for money. This confirms Anvuu et. al, (2006) assertion that a

structured approach putting into consideration all the necessary procurement arrangements

and project outcomes as well as laid down procedures is the best way to ascertain value for

money.

It was also realized that the Public Procurement Act contributed towards accountability. This

came to confirm Anvuur et. al (2006) research which spells out that when the five pillars of

the procurement are adhered to, it leads to the achievement of accountability. It once again

came out that the introduction of the Public Procurement Act has brought cost reduction. In

addition to that, the Public Procurement Act has led to the delivery of quality of service and

has brought transparency in the day - to - day activities of Suhum Government Hospital,

which confirms Anvuur et. al (2006) position.

54

Finally, it was established from the research that the 4 managers knew their roles when it

comes to procurement activities and had detailed knowledge of the Public Procurement Act

2003 (Act 663).

5.2 Conclusion

In conclusion, it was established through the research that, the staff of SGH knew about the

PPA which references could be made to it to serve as a guide during procurement activities.

The existence of the PPA as an Act of parliament seeks to achieve the procurement

procedure, judicious use of government and the hospital’s funds, fairness and equity in value

for money. It also came to light through the research that, the SGH does not have spacious

storage facility and the internal auditor checks every item before they are sent into or out of

the stores. It however came to light also that, there was a qualified stores personnel manager

in charge of the stores of SGH and the inception of the PPA has led to achieving health

services delivery since its inception or when it was passed into a law.

The research conducted can be used by by future researchers. The idea put across shows a

decision reached and a performance for each activity needs further testing and be validated

in other studies. The methodology used or implemented is an approach to a quantifiable and

step by step investigation. This research study has added to the knowledge in the area of the

implementation of the PPA in Ghana and recommendations prescribed. This study indicates

the benefits of looking at public procurement and the management of logistics towards

achieving accountability, transparency and value for money. What came out of the research

is a credible conclusion of what the PPA and management of logistics put together seeks to

achieve in terms of health services delivery in a district hospital in Ghana as a whole.

55

5.3 Recommendations

Depending on the findings of the study to achieve the stated objectives, the following

recommendations are made to improve the practice of procurement and management of

drugs in the Suhum Government Hospital.

Avoidance of Bureaucratic corruption

The Ministry of Health (MOH)/ Ghana Health Service (GHS) should ensure that, there is a

strict adherence to the Public Procurements Act to prevent collusion of top managers,

procurement officers and suppliers to manipulate the procurement system to their advantage.

In doing so, a lot of savings could be made to affect other areas which might need attention

to help proper delivery of health care to Ghanaians in general.

Training sessions

The MOH/GHS should ensure that, in service and external training should be organized for

GHS staffs preferably the procurement officers, Hospital Management Team (HMT) and

internal auditors to update them at regular interval the current issues that crop- up in the

procurement activities to enhance proper accountability, transparency, value for money and

availability of logistics (drug) in the various regional, district and other health facilities in

the country as a whole. This could even be fused into the thought courses of all health

related training institutions in the country in collaboration with the Universities,

Polytechnics and various health institutions.

56

Availability of funds

The government of Ghana as matter of importance must see to it that funds are readily made

available to all health institutions in the country to purchase adequate logistics (drugs) into

their stores to fore- store any intermittent shortages of very important drugs and non drugs

consumables in all health facilities.

Avoidance of corruption in the procurement system

To prevent or minimize corruption in the procurement processes of Suhum Government

Hospital, the researcher is of the opinion that, the suppliers of logistics for both drugs and

non-drugs should not be limited in terms of numbers of qualified suppliers but the hospitals

doors in terms of registration of suppliers should be opened and suppliers evaluated at

regular interval to check pricing and provision of quality products to the hospital.

Internal controls

In order to avoid waste and purchase of shoddy products, the management team of Suhum

Government Hospital should ensure that duties are segregated in terms of procurement to

ensure transparency and value for money. The government should also empower the Ghana

Audit Service (GAS) to initiate prosecution against whoever goes contrary to the Public

Procurement Act 2003 (Act 663).

Empowerment of the procurement committee of Suhum Government Hospital

The hospital management team should be committed to empower and strengthening their

procurement committee. This could be done based on at regular interval meetings with the

57

procurement committee members to give recommendations of products or items that would

be of importance to the hospital for a period.

58

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– 20 th

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U.S Agency for International Development. Available. (Assessed: 3 rd

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Hongkong.

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Eyiah, A K and Cook, P(2003) Financing small and medium –scale contractors in

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Guide.” Macmillan.

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Manso J. F, Annan J, &Anane S.S, (2013) “Assessment of Logistics Management in Ghana

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McGinnis, Michael A. and Jonathan W. Kohn (2002), “Logistics Strategy – Revisited,”

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Ministry of Finance (2001) “ Procurement Reform Proposal: A Component of the Public

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64

APPENDIXES

RESEARCH QUESTIONARE

This study is purely for academic purposes. All responses will be treated with due

confidentiality. This instrument is designed to draw out responses from employees at Suhum

Government Hospital as part of a research on the procedures of procurement and

management of Logistics (drugs) to assess whether they are effective and efficient to the

organization.

SECTION A: SOCIO- DEMOGRAPHIC CHARACTERISTICS

1. Gender a) male b) female

2. Grade/ Position at the hospital……………………………………………………….

3. Academic Qualification………………………………………………………………

SECTION B: EXISTENCE OF THE PUBLIC PROCUREMENT ACT

4. Have you heard about the Procurement Act? A) Yes b) No

5. If yes, how did you hear about it?

a) In the media

b) Through friends

c) During training/ staff durbar

d) At management meeting

65

e) Other (specify)

6. What do you know about the Public Procurement Act, 2003 (Act 663)?

a) A mere policy document

b) An Act of parliament

c) Procurement Guidelines

d) Tender Document

e) Other ( specify)

7. Have you seen or got a copy of the Public Procurement Act?

a) Yes b) No

8. If yes, have you been reading it or making it a reference source at regular interval?

a) Yes b) No

9. What does the Public Procurement Act seek to achieve?

a) Defined right procurement procedures

b) Judicious use of government funds

c) Equity and fairness

d) Value for money

e) Other (specify)

10. How is it applied at the hospital?

a) Highly agree

66

b) agree

c) disagree

d) strongly disagree

e) Other (specify)

SECTION C: COMPLIANCE WITH PPA WITH REGARDS TO STORES

RECEIPTS AND ISSUE PROCEDURE

11. Does your hospital have a store unit?

a) Yes b) No

12. Do you have qualified stores personnel?

a) Yes b) No

13. What is the educational background of the personnel?

a) MSLC/JSS b) SSCE/WACCE c) DBS d) HND

e) Other (specify) …………………………..

14. What is the rank of the stores personnel?

a) Storekeeper

b) Senior storekeeper

67

c) Principal storekeeper

d) Supply officer

e) Other (specify)

15. How many staff works at the stores?

a) One b) Two c) Three d) Four

e) Other (specify) ………………………….

16. Does your Hospital have spacious storage facilities at the stores?

a) Yes b No

17. What procedure does your hospital follow in accepting logistics (drugs) into the stores?

a) Listing all items in a notebook

b) Listing items in stock register

c) No documentations made upon receipt

d) Listing all items in the stores receipts voucher

e) Other (specify) ……………………………..

18. Who does the inspection and the quantity checks of stocks of logistics (drugs) received?

68

a) Internal auditor

b) Accountant

c) Storekeeper

d) Administrator

e) Other (specify) ……………………………….

SECTION D: CONTRIBUTIONS OF THE PUBLIC PROCUREMENT ACT

Please indicate by ticking the appropriate responses to each of the following indicators ( to a

larger extent, some extent, a little extent, to a no extent).

19. What is the effect of the implementation of the PPA on the performance of the hospital?

Effect of the Public

Procurement Act

To a larger

extent

To some

extent

To a little

extent

To a no

extent

a) Value for money

b) Accountability

c) Cost reduction

d) Quality of service

e) Transparency

69

20. What factors prevents employees from complying with the Public Procurement Act

theories at Suhum Government Hospital? Please tick the appropriate response to each of the

following indicators(strongly agree, agree, strongly disagree)

Factors Strongly

agree

Agree Disagree Strongly

disagree

a) Unclear statutory

basis and absence

of procurement

code

b) Inadequate

procurement

policy, strategy,

planning and

management

capacity

c) Poor procurement

organization and

procedures

d) Lack of qualified

procurement staff

e) Poor stock

management

f) Lack of availability

g) High prices

70

21. How can the above problems be controlled? …………………………………………

Awareness Level of the Public Procurement Cycle

22. Do you know about the existence of the Public Procurement Cycle?

a) Yes b) No

23. If yes, do you know the composition of the Public Procurement Cycle?

a) Yes b) No

24. If yes do you know the membership of the entity tender committee?

a) Yes b) No

25. What work does the evaluation members do? Please comment

…………………………………………………………………………………………………

…………………………………………………………………………………………………

…………………………………………………………………………………………………

………………

26. Do you have an annual procurement plan for the hospital?

a) Yes b) No

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SECTION E: INTERVIEW GUIDE

Open- ended question was used as an interview guide by the researcher. the interviewees of

which the interview guide was used were; the Medical Superintended of the Suhum

Government Hospital, the Health Services Administrator, the Accountant, the Head of

pharmacy and Head of the procurement unit. The questionnaires for the interviewees are

structured below;

1) Do you have annual procurement plan for the hospital?

2) What role do you play in procurement as a manger?

3) Do you know about the existence of the procurement cycle?

4) How well does the institution adhere to the procurement ACT?

5) What procedure does your hospital follow in accepting logistics (drugs) into stores?

Hypotheses__14 Jan 2020__01+-1.ppt

  • Research hypothesis
    A hypothesis can be defined as a prediction or explanation of the relationship between one or more independent variables (PREDISPOSING/RISK FACTORS) and one dependent variable (OUTCOME/CONDITION/DISEASE).
    A hypothesis, in other words, translates the problem statement into a precise, clear prediction of expected outcomes.

    It must be emphasized that hypotheses are not meant to be haphazard guesses, but should reflect the depth of knowledge, imagination and experience of the investigator.

    Therefore, in the process of formulating hypotheses, all variables relevant to the study should be identified.

*

Example: Health education involving active participation by mothers will produce more positive changes in child feeding than health education based on lectures.
Independent variable (predisposing factor): types of health education.
Dependent variable (outcome): changes in child feeding.

*

Research Traditions in the Use of Hypotheses

  • Hypotheses are always tentative
  • Research hypothesis, not the null hypothesis, is the focus of the research and presented in the research report

Hypotheses

An hypothesis is a specific statement of prediction. It describes in concrete (rather than theoretical) terms what you expect will happen in your study.

Not all studies have hypotheses.

A single study may have one or many hypotheses.

*

Hypotheses

Alternative /null hypothesis

Usually, we call the hypothesis that you support (your prediction) the alternative hypothesis, and we call the hypothesis that describes the remaining possible outcomes the null hypothesis.

*

Hypotheses

Sometimes we use a notation like HA or H1 to represent the alternative hypothesis or your prediction, and HO or H2 to represent the null case. You have to be careful here, though.

Alternative hypothesis __ HA or H1

Null hypothesis __ HO or H2

*

Hypothesis Testing

The Steps:

1. Describe in words the population characteristics about which the hypotheses are to be tested.

2. Define/ state the null hypothesis: H0 or H2

3.  Define/ state the alternative hypothesis: H1 or Ha

4.  State test statistic to be used associated with alpha [] the level of significance of the test.

Summary: Hypothesis Testing

The Steps:

5.     Calculate the p-value of what you observed

6.  State the conclusion / Reject or fail to reject (~accept) the null hypothesis

That is, decide whether to reject the null hypothesis or fail to reject the null hypothesis. The conclusion depends on the level of significance of the test. Also, remember to state your result in the context of the specific problem.

Hypotheses

Working example : Alternative /null hypothesis

For instance, let's imagine that you are investigating the effects of a new employee training programme and that you believe one of the outcomes will be that there will be less employee absenteeism. Your two hypotheses might be stated something like this:

*

Hypotheses

Working example: Alternative /null hypothesis

The null hypothesis for this study is:

HO: As a result of the XYZ company employee training program, there will either be no significant difference in employee absenteeism or there will be a significant increase.

Which is tested against the alternative hypothesis:

HA: As a result of the XYZ company employee training program, there will be a significant decrease in employee absenteeism.

*

Hypotheses

Keep in mind: Alternative /null hypothesis

The important thing to remember about stating hypotheses is that you formulate your prediction (directional or not), and then you formulate a second hypothesis that is mutually exclusive of the first and incorporates all possible alternative outcomes for that case.

*

Hypotheses

Keep in mind: Alternative /null hypothesis

If your prediction was correct, then you would (usually) reject the null hypothesis and accept the alternative.

If your original prediction was not supported in the data, then you will accept the null hypothesis and reject the alternative.

*

Common “Z” levels of confidence

Commonly used confidence levels are 90%, 95%, and 99%

Confidence Level

Z value

1.28

1.645

1.96

2.33

2.58

3.08

3.27

80%

90%

95%

98%

99%

99.8%

99.9%

Statistically Significant

Denote:

** = 1 percent

* = 5 percent

*** = 10 percent

Statistics vs. Parameters

Sample Statistic

– any summary measure calculated from data; e.g., could be a mean, a difference in means or proportions, an odds ratio, or a correlation coefficient

Population Parameter

– the true value/true effect in the entire population of interest

*

The P-value

  • P-value is the probability that we would have seen our data (or something more unexpected) just by chance if the null hypothesis (null value) is true.

  • Small p-values mean the null value is unlikely given our data.

P values

P values = the probability that the observed result was obtained by chance

  • i.e. when the null hypothesis is true

α level is set a priori (Usually 0.05)

If p < α level then we reject the null hypothesis and accept the experimental hypothesis

  • 95% certain that our experimental effect is genuine

If however, p > α level then we reject the experimental hypothesis and accept the null hypothesis

*

P = Probability

this value tells us the probability that the observed result was obtained by chance

That there is no difference between the two groups

Each test result (e.g. t value) is associated with a particular p value

α level is set a priori

This is basically an acceptance level

Usually this is set to 0.05

But as I understand, α levels are usually much lower than this in fMRI

If p < α level then we reject the null hypothesis and accept the experimental hypothesis

- concluding that we are 95% certain that our experimental effect is genuine

If however, p > α level then we reject the experimental hypothesis and accept the null hypothesis

- that there was no sig diff in brain activation levels between the two conditions


The Meaning of the "p Value" from a Test

  • The end result of a statistical significance test is a p value, which represents the probability that random fluctuations alone could have generated results that differed from the null hypothesis (H0), in the direction of the alternate hypothesis (HAlt), by at least as much as what you observed in your data.

If this probability is too small, then H0 can no longer explain your results, and you're justified in rejecting it and accepting HAlt, which says that some real effect is present. You can say that the effect seen in your data is statistically significant.

  • How small is too small for a p value? This determination is arbitrary; it depends on how much of a risk you're willing to take of being fooled by random fluctuations (that is, of making a Type I error). Over the years, the value of 0.05 has become accepted as a reasonable criterion for declaring significance.
  • If you adopt the criterion that p must be less than or equal to 0.05 to declare significance, then you'll keep the chance of making a Type I error to no more than 5 percent.

11.*

Concepts of Hypothesis Testing

The two possible decisions that can be made:

Conclude that there is enough evidence to support the alternative hypothesis

(also stated as: reject the null hypothesis in favor of the alternative)

Conclude that there is not enough evidence to support the alternative hypothesis

(also stated as: failing to reject the null hypothesis in favor of the alternative)

NOTE: we do not say that we accept the null hypothesis if a statistician is around…

11.*

Interpreting the p-value…

The smaller the p-value, the more statistical evidence exists to support the alternative hypothesis.

If the p-value is less than 1%, there is overwhelming evidence that supports the alternative hypothesis.

If the p-value is between 1% and 5%, there is a strong evidence that supports the alternative hypothesis.

If the p-value is between 5% and 10% there is a weak evidence that supports the alternative hypothesis.

If the p-value exceeds 10%, there is no evidence that supports the alternative hypothesis.

11.*

Interpreting the p-value…

Overwhelming Evidence

(Highly Significant)

Strong Evidence

(Significant)

Weak Evidence

(Not Significant)

No Evidence

(Not Significant)

0 .01 .05 .10

p=.0069

11.*

Conclusions of a Test of Hypothesis…

If we reject the null hypothesis, we conclude that there is enough evidence to infer that the alternative hypothesis is true.

If we fail to reject the null hypothesis, we conclude that there is not enough statistical evidence to infer that the alternative hypothesis is true. This does not mean that we have proven that the null hypothesis is true!

Keep in mind that committing a Type I error OR a Type II error can be VERY bad depending on the problem.

Type I Error

H0 ------true

But we reject H0

Example: Innocent but found guilty.

  • Type II Error

  • H0 ------false
  • But we fail to reject H0

Example: Guilty but found innocent

Two types of errors

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Hypothesis Testing…

The probability of a Type I error is denoted as α (Greek letter alpha). The probability of a type II error is β(Greek letter beta).

  • The two probabilities are inversely related. Decreasing one increases the other, for a fixed sample size.

In other words, you can’t have  and β both real small for any old sample size. You may have to take a much larger sample size, or in the court example, you need much more evidence.

Types of Errors

There are two types of error that you can make:

A Type 1 (or alpha) error denotes a false positive result, i.e. that you accept the H1 in your data, even though the H0 is true

Conversely, a type 2 (or beta) error denotes a false negative result, i.e. that you accept the H0, even though the H1 is true

The two green fields describe the remaining probability that, given alpha (or beta), you are making the correct decision when you accept the H0 (true negative result) or reject the H0 (i.e. accept the H1) (true positive result)

The way in which we decide whether a given value is highly unlikely (i.e. Statistically significant) is to look at the underlying distribution

Population
H0 H1
Sample H0 1-a b-error (Type II error)
H1 a-error (Type I error) 1-b

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Two types of errors

Type I error = false positive

  • α level of 0.05 means that there is 5% risk that a type I error will be encountered

Type II error = false negative

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Beware of errors

We must be aware that errors can occur during this process

Two types of errors

- type I error = false positive

Where we incorrectly reject the null hypothesis.

The pre-determined α level determines the risk of this type of error.

α level of 0.05 means that there is 5% risk that a type I error will be encountered.

The other type of error is…

- type II error = false negative

Where we incorrectly reject the exp hypothesis

Significant?

11.bin

The P-value

By convention, p-values of <.05 are often accepted as “statistically significant”

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Example: Health education involving active participation by mothers will produce more positive changes in child feeding than health education based on lectures.
Independent variable (predisposing factor): types of health education.
Dependent variable (outcome): changes in child feeding.

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P = Probability

this value tells us the probability that the observed result was obtained by chance

That there is no difference between the two groups

Each test result (e.g. t value) is associated with a particular p value

α level is set a priori

This is basically an acceptance level

Usually this is set to 0.05

But as I understand, α levels are usually much lower than this in fMRI

If p < α level then we reject the null hypothesis and accept the experimental hypothesis

- concluding that we are 95% certain that our experimental effect is genuine

If however, p > α level then we reject the experimental hypothesis and accept the null hypothesis

- that there was no sig diff in brain activation levels between the two conditions

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Beware of errors

We must be aware that errors can occur during this process

Two types of errors

- type I error = false positive

Where we incorrectly reject the null hypothesis.

The pre-determined α level determines the risk of this type of error.

α level of 0.05 means that there is 5% risk that a type I error will be encountered.

The other type of error is…

- type II error = false negative

Where we incorrectly reject the exp hypothesis

Decision Chart for Significance

Meaning: there is a

sig. relationship. b/w

the two variables

it is sig.

if prob. smaller than

0.05

Meaning: there is No

Sig. relationship

b/w the two

variables

it is Not Sig.

if prob. bigger than

0.05

Is It significant?

Qualitative-sampling-techniques-01+-1.pdf

Objective of Presentation

By the end of this presentation you should be able to:

• Describe the justification of qualitative Sampling Techniques

• Understand different types of Sampling Techniques

Sampling for Qualitative

Research

• The aim of the qualitative research is to understand, from within, the subjective reality of the study participants.

• This will not be achieved through superficial knowledge about a large, representative sample of individuals.

• Rather we want to reach people within the study area who can share their unique slice of reality, so that all slices together illustrate the range of variation within the study area.

Sampling for Qualitative

Research

• The general rule in qualitative research is that you continue to sample until you are not getting any new information or are no longer gaining new insights.

“Saturation”

Sampling for Qualitative

Research

• With careful sampling and equally careful collection techniques, a surprisingly small number of interviews, narratives or focus groups can yield the data to answer your research question.

Types of Sampling

Convenience Sampling

(ease of access)

• Convenience sampling defined as a group of individuals believed to be representative of the population from which it is selected, but chosen because it is close at hand rather than being randomly selected. – Selection of the sample is at the convenience of the

researcher – Biased

• e.g. when you simply ask any patient in your clinic who is willing to participate.

Convenience

• Saves time, money, and effort. Poorest rational; lowest credibility. Yields information-poor cases.

• This is the least rigorous technique, involving the selection of the most accessible subjects. It is the least costly to the researcher, in terms of time, effort and money, but may result in poor quality data and lacks intellectual credibility.

• There is an element of convenience sampling in many qualitative studies, but a more thoughtful approach to selection of a sample is usually justified.

Theoretical Sample

The process of data collection for generating

theory whereby the analyst jointly collects, codes, and analyzes his data and decides what data to collect next and where to find them, in order to develop the theory as it emerges”

(Glaser and Strauss, 1967)

• The sampling process is entirely controlled by the emerging theory

Purposive Sampling

(judgemental)

• The researcher attempts to obtain sample that appears to him/her to be representative of the population.

Snowball Sampling

(friend of friend)

• You initially contact a few potential respondents and then ask them whether they know of anybody with the same characteristics that you are looking for in your research.

• For example, if you wanted to interview a sample of vegetarians / cyclists / people with a particular disability / people who support a particular political party etc.

Extreme or Deviant Case

• Learning from highly unusual manifestations of the phenomenon of interest, such as outstanding success/notable failures, top of the class/dropouts, exotic events, crises.

• To obtain information on unusual cases, which can be especially ‘problematic’ or especially ‘good’.

Intensity

• Information-rich cases that manifest the phenomenon intensely, but not extremely, such as good students/poor students, above average/below average.

Maximum Variation

• Purposefully picking a wide range of variation on dimensions of interest...documents unique or diverse variations that have emerged in adapting to different conditions. Identifies important common patterns that cut across variations.

• To obtain information about the significance of various

circumstances for processes and outcome (e.g. three to four cases that are very different on one dimension: e.g. largest, median and smallest size; government, aided, not-for-profit and commercial funding patterns; city, town and rural area).

Homogeneous

• Focuses, reduces variation, simplifies analysis, facilitates group interviewing.

Typical Case

• Illustrates or highlights what is typical, normal, average.

• The case is specifically selected because it is not in any way atypical, extreme, deviant or intensely unusual.

• This strategy is often used when the units of analysis are large, as for example in studies of villages in developing countries.

• Selecting a typical village allows the research to illustrate the general process that occurs.

• This strategy is particularly useful if the research report will predominantly be read by people who are unfamiliar with the area of research.

Stratified Purposeful

• Illustrates characteristics of particular subgroups of interest; facilitates comparisons.

• The technique is a kind of ‘statistically non representative stratified sampling’ because, while it is similar to its quantitative counterpart, it must not be seen as a sampling strategy that allows statistical generalisation to the large population.

Critical Case

• Permits logical generalization and maximum application of information to other cases because if it's true of this once case it's likely to be true of all other cases.

• To test a hypothesis by choosing the case that permits logical deductions of the type, “If this is valid for this case, then it should apply to all cases.” Or “If it is not valid for this case, it is unlikely to be valid for any other cases”.

Key Informant Sample

• Key informants, as a result of their personal skills, or position within a society, are able to provide more information and a deeper insight into what is going on around them.

• Characteristics of an "ideal" key informant: 1. Role in community 2. Knowledge 3. Willingness 4. Communicability 5. Unbiased

Criterion

• All cases that meet a set of criteria are selected. In criterion sampling it is important to select the criteria carefully, so as to define cases that will provide detailed and rich data relevant to the particular research problem.

• For example, all former clients of an intensive care unit who return to intensive care with the same complaint within three weeks may constitute a sample for in-depth, qualitative study.

• These criteria would facilitate a study of the effectiveness of after-care programs attached to intensive care units.

Confirming or Disconfirming

• Elaborating and deepening initial analysis, seeking exceptions, testing variation.

Opportunistic

• New opportunities to recruit participants or to gain access to a new site may develop after the fieldwork has begun.

• A researcher studying heart attacks may, for example, meet a cardiologist while interviewing one of his or her patients.

• The cardiologist may suggest how the researcher can contact other cardiologists who would be willing to refer clients to the researcher.

Random Purposeful

• (still small sample size) Adds credibility to sample when potential purposeful sample is larger than one can handle. Reduces judgment within a purposeful category. (Not for generalizations or representativeness)

Politically Important Cases

• Attracts attention to the study (or avoids attracting undesired attention by purposefully eliminating from the sample politically sensitive cases).

Volunteer Sampling

• Samples are often drawn through advertising, requesting people to volunteer to participate in the study.

• This can be particularly useful when potential participants are dispersed throughout the community or difficult to contact directly.

• However, volunteer samples are typically biased in particular ways.

• For example, a volunteer sample of people living with HIV/AIDS will systematically be biased to exclude people who are denying or ignoring their HIV status.

Combination or Mixed

Purposeful

• Triangulation, flexibility, meets multiple interests and needs.

End

Research Methods __ Feb - May __2020__.ppt

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The illiterate of the 21st Century will not be those who cannot read and write, but those who cannot learn, unlearn, and relearn (Alvin Torffler, 2006).

“Kindness in an inner desire that makes us want to do good things even if we do not get anything in return. It is the joy of our life to do them. When we do good things from this inner desire, there is kindness in everything we think, say, want and do” (Emmanuel Swedenborg, 1688 – 1722).

“The surest way to keep people down is to educate the men and neglect the women. If you educate a man you simply educate an individual, but if you educate a woman, you educate a whole nation“ (James Kwegyir Aggrey).

SPSG 704 :Research Methods
Welcome August 10th - 28th 2020

“-- First you must say what you are going to talk about,

--- Secondly you must talk about it,

--- And then you must say what you talked about”

(Alistair Cooke: 2004:14)

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Definitions of Research

The main goal of research is the gathering and interpreting of information to answer questions (Hyllegard, Mood, & Morrow, 1996).

Research is a systematic attempt to provide answers to questions (Tuckman, 1999).

Research may be defined as the systematic and objective analysis and recording of controlled observations that may lead to the development of generalizations, principles, or theories, resulting in prediction and possible control of events (Best & Kahn, 1998).

Research is a systematic way of asking questions, a systematic method of inquiry (Drew, Hardman, & Hart, 1996).

Source: https://www.hanze.nl/en/research/researchportal/centre-of-applied-research-and-innovation/art-society/lifelong-learning-in-music/knowledge-base/online-research-coach/pages/what-is-research.aspx?wbc_purpose=B

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Development of Research Skills

Learning how to conduct good research:

  • Review knowledge base
  • New skills (that many people do not have)
  • Better understanding and interpretation of the literature
  • Recognize new questions that need investigation

Objectivity is the key element of research

Search for Truth

Five sources of evidence in the pursuit of truth:

Custom and tradition

Authority

Personal experience

Deductive reasoning

Scientific inquiry

Code of Conduct for Practice Based Research for Higher Professional Education (HPE)

1. Researchers in HPE serve a professional and a social interest

They will contribute to the profession and the professional practice concerned and they will make an effort to serve public interest. They will focus on relevant themes and problems from the professional practice and on creative, innovative and applicable solutions for the professional practice.

They will contribute to the development of knowledge and theory, stimulate knowledge circulation about both practice and education and they will strive for making results accessible according to the principles of Open Access.

Code of Conduct for Practice Based Research for Higher Professional Education (HPE)

2. HPE researchers are respectful

They will take into consideration the rights, interests, privacy, viewpoints, beliefs, theories and methods of those involved and of fellow researchers. They will abide by the rules and protocols which apply to the professional practice for doing research.

Should research with people and animals give rise to any risk, the interest of the research should justify the taking of that risk. In this case external advisors will be consulted.

Code of Conduct for Practice Based Research for Higher Professional Education (HPE)

3. HPE Researchers are careful

They will consider various scientific viewpoints and related forms of research, the available research methods and the methodological rules which are part of this, as well as the research and professional ethics and values which apply to the professional practice concerned. They will make use of available knowledge from the professional practice and science.

They will write reports which are accurate, complete, exact and replicable. They will take into consideration the desirability of a careful preservation of the data and make sure that intellectual property rights concerning data, results and innovations have been properly dealt with.

Code of Conduct for Practice Based Research for Higher Professional Education (HPE)

4. HPE Researchers have integrity

They will be critical concerning opinions and problem definitions held in the professional practice, independent in their choices of method and honest about the sources they use.

They will be communicative about their behaviour during the carrying out of the research, autonomous in their analyses and impartial in their reports.

Code of Conduct for Practice Based Research for Higher Professional Education (HPE)

5. HPE Researchers are answerable for their choices and behaviour

They will be accountable concerning the relevance of their chosen theme, their choice of research setup and the used methods and their restrictions, the care concerning the carrying out, the underpinning of the conclusions, the sources consulted, the implementation in the professional practice, as well as the way it will affect education.

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Course Outline & Topics

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Qualitative and Quantitative Paradigms

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Qualitative and Quantitative Paradigms

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Qualitative and Quantitative Paradigms

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Qualitative and Quantitative Paradigms

Subject matter of lecture: Research Methods __ Overview

Qualitative Research Quantitative Research

Also known as interpretative / responsive positivist /hypothetico-deductive

Type of reasoning (usually) inductive (usually) deductive

Link with concepts identifies concepts identified concepts and

investigates relationships

Action sometimes only describes a situation tests relationships between

But in action-research openly intervene concepts

Outcome illuminates the situation accepts or rejects proposed theory

Approach to validity truth seen as context bound truth seen as objective and

(socially constructed) universal

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Typical Methods

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Typical Methods

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Research Methods Categorised by Activity

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Research Methods Categorised by Activity

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Research Methods Categorised by Activity

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Software

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Conclusion

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YOUR RESEARCH PROJECT HOW & WHERE TO START?

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YOUR RESEARCH PROJECT HOW & WHERE TO START?

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Science and Research

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Science and Research

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Science and Research

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Science and Research

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Science and Research

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Science and Research

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Science and Research

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Science and Research

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Science and Research

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Science and Research

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Science and Research

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Science and Research

According to Plato, knowledge is a subset of that which is both true and believed

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Scientific Revolution

The event which many historians of science call the scientific revolution can be dated roughly as having begun in 1543, the year in which Nicolaus Copernicuss published his De revolutionibus orbium coelestiumm (On the Revolutions of the Heavenly Spheres) and Andreas Vesaliuss published his De humani corporis fabrica (On the Fabric of the Human body).

As with many historical demarcations, historians of science disagree about its boundaries. Although the period is commonly dated to the 16th and 17th centuries, some see elements contributing to the revolution as early as the middle ages, and finding its last stages in chemistry and biology in the 18th and 19th centuries.


Scientific Revolution

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Significance of the Revolution

Many contemporary writers and modern historians claim that there was a revolutionary change in world view. In 1611 the English poet, John Donnee, wrote:

[The] new Philosophy calls all in doubt,
The Element of fire is quite put out;
The Sun is lost, and the earth, and no man's wit
Can well direct him where to look for it


Scientific Revolution

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Renaissance

The Renaissance (meaning "rebirth"; Italian: Rinascimento, from re- "again" and nascere "be born") was a cultural movement that spanned roughly the 14th through the 17th century, beginning in Italy in the late Middle Ages and later spreading to the rest of western Europe.


Scientific Revolution

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Renaissance

The Renaissance saw developments in most intellectual pursuits, but is perhaps best known for its artistic aspect and the contributions of such polymaths as Leonardo da Vinci and Michelangelo, who have inspired the term "Renaissance men".


Scientific Revolution

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Paradigm Shifts

The Structure of Scientific Revolutions (SSR) Kuhn argued that science does not progress via a linear accumulation of new knowledge, but undergoes periodic revolutions, also called "paradigm shifts" (although he did not coin the phrase), in which the nature of scientific inquiry within a particular field is abruptly transformed.

In general, science is broken up into three distinct stages. Prescience, which lacks a central paradigm, comes first.


The Structure of Scientific Revolutions

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Paradigm Shifts

This is followed by "normal science", when scientists attempt to enlarge the central paradigm by "puzzle-solving". Thus, the failure of a result to conform to the paradigm is seen not as refuting the paradigm, but as the mistake of the researcher.

As anomalous results build up, science reaches a crisis, at which point a new paradigm, which subsumes the old results along with the anomalous results into one framework, is accepted. This is termed revolutionary science.



The Structure of Scientific Revolutions

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  • Kuhn was married twice, first to Kathryn Muhs (with whom he had three children) and later to Jehane Barton (Jahane R. Kuhn)


The Structure of Scientific Revolutions

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The Polanyi-Kuhn Debate

Scientific historians and scholars have noted similarities between Kuhn's work and the work of Michael Polanyi.

Although they used different terminologies, both scientists believed that scientists' subjective experiences made science a relativistic discipline. Polanyi lectured on this topic for decades before Kuhn published "The Structure of Scientific Revolutions."



The Structure of Scientific Revolutions

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The Polanyi-Kuhn Debate

Supporters of Polanyi charged Kuhn with plagiarism, as it was known that Kuhn attended several of Polanyi's lectures, and that the two men had debated endlessly over the epistemology of science before either had achieved fame.

In response to these critics, Kuhn cited Polanyi in the second edition of "The Structure of Scientific Revolutions," and the two scientists agreed to set aside their differences in the hopes of enlightening the world to the dynamic nature of science.

  • Despite this intellectual alliance, Polanyi's work was constantly interpreted by others within the framework of Kuhn's paradigm shifts, much to Polanyi's (and Kuhn's) dismay.


The Structure of Scientific Revolutions

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The Purpose of Research __ why conduct social science research?

Where can you find people conducting social research, students, professors, professional researchers, and scientists' in universities, research centers, and the government, with an army of assistants and technicians, conduct much social research.

This research is not visible to the average person. Although the results may appear only in specialized publications or textbooks, the basic knowledge and research methods that professional researchers develop become the basis for all other social research.


Why Conduct Social Science Research

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The Purpose of Research __ why conduct science social research?

In addition to those in universities, people who work for newspapers, television networks, market research firms, schools, hospitals, social service agencies, political parties, consulting firms, government agencies, personnel departments, public interest organizations, insurance companies, or law firms may conduct research as part of their jobs.


Why Conduct Social Science Research

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The Purpose of Research __ why conduct science social research?

The findings from research yield better informed, less biased decisions than the guessing, hunches, and intuition:

Science does not, and cannot, provide people with fixed, absolute truth. This is because science is a slow, incomplete process of reducing untruth. It is a quest for the best possible answers carried out by a collection of devoted people who labour strenuously in a careful, systematic, and open-minded manner.

Many people are uneasy with the pain-staking pace, hesitating process, and incertitude of science. They demand immediate, absolute answers. Many turn to religious fanatics or political demagogues who offer final, conclusive truths in abundance.


Why Conduct Social Science Research

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The Purpose of Research __ why conduct science social research?

What does this mean for diligent practitioners (e.g., human service workers, health care professionals, criminal justice officers, journalists, peace and security analyst, econometrist or policy analysts) who have to make prompt decisions in their daily work?

Must they abandon scientific thinking and rely on common sense, personal conviction, or political doctrine? No.


Why Conduct Social Science Research

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The Purpose of Research __ why conduct social science research?

They too, can use social scientific thinking. Their task is difficult but possible. They must conscientiously try to locate the best knowledge currently available; use careful, independent reasoning; avoid known errors or fallacies; and be wary of any doctrine offering complete, final answers.

Practitioners must always be open to new ideas, use multiple information source and constantly question the evidence offered to support a course of action.



Why Conduct Social Science Research

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The Purpose of Research __ why conduct science social research?

Misuse:

Unfortunately, those being studied may feel over studied or overloaded by the research. For example, the many exit poll studies by the mass media during elections have prompted a backlash of people refusing to vote and debates over legal restrictions on such polling.

Also, some people misuse or abuse social research– use sloppy research techniques, misinterpret findings, rig studies to find previously decided results, and so on. But the hostile reactions to such misuse may be directed at research in general instead of at the people who misuse it.



Why Conduct Social Science Research

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The Purpose of Research __ why conduct social science research?

The Use of Research:

For over a century, social research has had two wings.

Researchers in one adopt a more detached, scientific, and academic orientation; those in the other are more activist, pragmatic, and reform oriented. This is not a rigid separation.

Researchers in the two wings cooperate and maintain friendly relations. Some move from one wing to another at different stages in their careers.


Why Conduct Social Science Research

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The Purpose of Research __ why conduct social science research?

The Use of Research:

The difference in orientation revolves around how to use social research. In simple terms, some focus on using research to advance general knowledge, whereas others use it to solve specific problems.

Those who seek an understanding of the fundamental nature of social reality are engaged in basic research (also called academic research or pure research).

Applied researchers, by contrast, primarily want to apply and tailor knowledge to address a specific practical issue. They want to answer policy question or solve a pressing social problem.


Why Conduct Social Science Research

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The Purpose of Research __ why conduct social science research?

The Use of Research:

Basic Research:

Basic research advances fundamental knowledge about the social world.

It focuses on refuting or supporting theories that explain how the social world operates, what makes things happen, why social relations are a certain way, and why society changes.

Basic research is the source of most new scientific ideas and ways of thinking about the world. It can be exploratory, descriptive, or explanatory; however, explanatory research is the most common.


Why Conduct Social Science Research

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The Purpose of Research __ why conduct social science research?

The Use of Research:

Basic Research:

Many non-scientists criticize basic research and ask, ‘’What good is it ?’’

They consider basic research to be a waste of time and money because it does not have a direct use or help resolve an immediate problem.

It is true that knowledge produced by basic research often lacks practical applications in the short term.


Why Conduct Social Science Research

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The Purpose of Research __ why conduct social research?

The Use of Research:

Basic Research:

Yet , basic research provides a foundation for knowledge and understanding that are generalizable to many police areas, problems, or areas of study.

Basic research is the source of most of the tools – methods, theories, and ideas – that applied researchers use. Really big break through’s in understanding and significant advances in knowledge usually come from basic research.


Why Conduct Social Science Research

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The Purpose of Research __ why conduct social science research?

The Use of Research:

Applied Research:

Applied researchers try to solve specific policy problems or help practitioners accomplish tasks.

Theory is less central to them than seeking a solution to a specific problem for a limited setting (e.g. ‘’Will the number of auto accidents involving drunk drivers decline if governments sponsor road safety educational programmes?’’).

Applied research is frequently descriptive research, and its main strength is its immediate practical use.



Why Conduct Social Science Research

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The Purpose of Research __ why conduct social science research?

The Use of Research:

Applied Research:

People employed by businesses, government agencies, social service agencies, health organizations, and educational institutions conduct applied research. It often affects our daily lives.

Decisions to market a new product, to choose one policy over another, or to continue or end a public programme may be based on applied research.



Why Conduct Social Science Research

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The Purpose of Research __ why conduct social science research?

The Use of Research:

Applied Research:

The scientific community is the primary consumer of basic research. The consumers of applied research findings are practitioners such as teachers, counsellors', and caseworkers, or decision makers such as managers, committees, and officials. Often, someone other than the researcher who conducted the study uses the results of applied research.

The use of the results may be beyond the researcher’s control. This means that applied researchers have an obligation to translate findings from scientific technical language into the language of decision makers or practitioners.


Why Conduct Social Science Research

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The Purpose of Research __ why conduct social science research?

The Use of Research:

Applied Research:

Applied and basic researchers adopt different orientations toward research methodology. Basic researchers emphasize high scientific standards and try to conduct near-perfect research.

Applied researchers make some more trade-offs. They may compromise scientific rigor to get quick, usable results.

Compromise is no excuse for sloppy research, however.


Why Conduct Social Science Research

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Basic and Applied Research Compared

Basic Applied

Research is intrinsically satisfying and Research is part of a job and is judged by sponsors

judgements are by other researchers. who are outside the discipline .

Research problems and subjects are Research problems are narrowly constrained to

selected with a great deal of freedom. the demands of employers or sponsors.

Research is judged by absolute norms of The rigor and standards of scholarships depend on

scientific rigor, and the highest standards the uses of results. Research can be quick and dirty

of scholarship are sought. or may match high scientific standards.

The primary concern is with the internal The primary concern is with the ability to generalize

logic and rigor of research design findings to areas of interest to sponsors.

The driving goal is to contribute to basic The driving goal is to have practical payoffs or uses

theoretical knowledge for results

Success comes when results appear in a Success comes when results are used by sponsors in

scholarly journal and have an impact on in decision making

others in the scientific community

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Why Conduct Social Science Research

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Acquiring knowledge

Issues

As the previous discussion has shown, a distinguishing characteristic of science is the method by which we know something to be true. This can be highlighted by contrasting science to other methods of knowing reality.

Charles Peirce (in Kerlinger, 1986), classified methods of knowing, or as he called them, methods of “fixing belief” into four categories:

1.) the method of tenacity,

2.) the method of authority,

3.) the “a priori” method and finally,

4.) the method of science.



Methods of Obtaining Knowledge

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Acquiring knowledge__ Types of Knowledge

Method of Tenacity

Probably the least sophisticated method for fixing belief is the method of tenacity.

This method determines truth, or establishes explanations, by asserting that something is true simply because it is commonly known to be true. This sometimes-fanatical adherence to a set of beliefs is exemplified in racial or ethnic stereotypes.

In the method of tenacity, the process of formulating beliefs occurs entirely within a given individual and is entirely subject to that person’s beliefs, values and idiosyncrasies.



Methods of Obtaining Knowledge

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Acquiring knowledge__ Types of Knowledge

Method of Tenacity

Although it is the most primitive, this method of forming beliefs about what is true and what is false is very commonly used.

Remarkably, people often sustain belief even in the face of contrary evidence.



Methods of Obtaining Knowledge

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Acquiring knowledge__ Types of Knowledge

Method of Authority

The second method is the method of authority.

In this method, truth is established when someone or something for which I have high regard states the truth.

I may accept my physician’s diagnosis of my illness as truth because my physician has been correct in the past, or because I have been taught that physicians are expert in what they do.

Or I may regard a religious text or a political tract as the distillation of truth, because they are sources of authoritative statements.



Methods of Obtaining Knowledge

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Acquiring knowledge__ Types of Knowledge

Method of Authority

The second method is the method of authority.

This method has an advantage over the method of tenacity because it often relies on the testimony of experts. If the source is indeed expert, adopting the expert’s advice may be beneficial.

In the case of medical problems, for instance, it would be better if a patient followed a doctor’s expert advice rather than clinging to the personal belief that his condition was the consequence of the wrath of a vengeful god and not subject to cure.


Methods of Obtaining Knowledge

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Acquiring knowledge__ Types of Knowledge

Method of Authority

The method of authority is quite widespread and has both its uses and abuses. The number of people who make a living as consultants, or purveyors of expertise, testifies to its popularity.

The method is also used in advertising, as products are ringingly endorsed by people who “ought to know”, such as a champion tennis player endorsing a racquet.

But this method is dangerous when the purported expert is really not knowledgeable (such as a medical “quack”), or when persons with expertise in one area give advice in an unrelated area (a movie star endorses a political candidate).



Methods of Obtaining Knowledge

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Acquiring knowledge__ Types of Knowledge

A priori and a posteriori (philosophy)

The third method of knowing is the a priori method or the method of reasonable men.

This method rests on the idea that the propositions submitted are self-evident, that is, they agree with reason.

The terms "a priori" and "a posteriori" are used in philosophy to distinguish between deductive and inductive reasoning, respectively. Attempts to define clearly or explain a priori and a posteriori knowledge are part of a central thread in epistemology, the study of knowledge.

One rough and oversimplified explanation is that a priori knowledge is independent of experience, while a posteriori knowledge is dependent on experience.


Methods of Obtaining Knowledge

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Acquiring knowledge__ Types of Knowledge

Scientific method __last method

What is Science?

Science is the concerted human effort to increase knowledge and understanding of the natural world and how the natural world works. Approaches to scientific work have evolved throughout history to well-defined knowledge acquisition workflows, techniques and standards, which are known as the ‘scientific method’. Following the principles of the scientific method is the essence of academic work. In your Master Thesis you will demonstrate the academic working skills that you have acquired during your MSc/MA study.

In the future you will find these skills useful in asking the right questions, the approach to problem solving and presenting what you have done. Academic work is not restricted to academia!

Source: https://elearn.sbg.ac.at/webapps/blackboard/content/listContent.jsp?course_id=_34719_1&content_id=_1023514_1



Methods of Obtaining Knowledge

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Acquiring knowledge__ Types of Knowledge

Scientific method __last method

  • Scientific method refers to the body of techniques for investigating phenomena, acquiring new knowledge, or correcting and integrating previous knowledge.
  • It is based on gathering observable, empirical and measurable evidence subject to specific principles of reasoning.

  • A scientific method consists of the collection of data through observation and experimentation, and the formulation and testing of hypotheses.



Methods of Obtaining Knowledge

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Acquiring knowledge__ Types of Knowledge

Scientific method __last method

Among other facets shared by the various fields of inquiry is the conviction that the process must be objective to reduce a biased interpretation of the results.

Another basic expectation is to document, archive and share all data and methodology so they are available for careful scrutiny by other scientists, thereby allowing other researchers the opportunity to verify results by attempting to reproduce them.

This practice, called full disclosure, also allows statistical measures of the reliability of these data to be established.


Methods of Obtaining Knowledge

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Acquiring knowledge__ Types of Knowledge

Scientific method __last method

Limitations of the Scientific Method

The scientific method cannot be used to study all questions. We cannot employ the scientific method when objective observation is not possible. In this case, we must use other methods of fixing belief.


Methods of Obtaining Knowledge

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Acquiring knowledge__ Types of Knowledge

Scientific method __last method

Limitations of the Scientific Method

  • Basic beliefs or assumptions are not testable propositions, as they can never be disproved, and thus they cannot be investigated scientifically. The statement that “We take these truths to be self-evident … that all men are created equal….” is a wonderful statement of personal truth, but it lies outside the realm of scientific investigation.

The limits of science are clear; the limits of belief are not.

Refer to slides: Religion and science _Best_011.ppt



Methods of Obtaining Knowledge

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Exercise __ in-class

Discuss the two main categories of research methodology; make your preference known.

What matters is that the methodology used fit the intended purposes of the research. Do you agree with this statement; justify your position.

Within the philosophy of science and research, we are informed “truth” is important. As a graduate student and, in view of the quagmire in Iraq and Afghanistan how would you explain this statement to your undergraduate friend.


Methods of Obtaining Knowledge

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Ontology - The Nature of “Reality”

Realism

There is an independently existing “objective” reality.

Idealism

“Reality” exists only in our minds

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Epistemology - The Nature of Knowledge and Meaning

1. Objectivism

Meaning exists in the world.

2. Constructionism

Meaning comes from our interactions with the world (and others).

3. Subjectivism

We impose meaning on the world.

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Epistemology Personified

Image Sources: http://www.synthstuff.com/mt/archives/bertrand-miner.jpg ; http://www.hmoon.com/garments/cloaks/traveler1-detail.jpg; http://newsimg.bbc.co.uk/media/images/42854000/jpg/_42854125_activist_afp416.jpg

Objectivism

Subjectivism

Constructionism

The Miner

The Traveler

The Activist

Meaning exists in the world

Meaning comes from our interactions with the world

We impose meaning on the world

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Contrasting the Approaches

Objectivism Construct- ionsim Subjectivism
Goal: Explanation Exploration Empowerment
Looking for: Truth Understanding Progress
Subjectivity: Error Embrace Multiple Perspectives Specific Perspective
Look at: States (static) , Pieces Processes (active), Whole Processes (active), Whole
Design is: Pre-planned Emergent Emergent
Logic: Causal, “hard” Constraining Factors, “Soft” Constraining Factors, “Soft”
Extrapolation: Generalization (given representative sampling) Lessons learned, “petite generalizations” Lessons learned, “petite generalizations”
Kinds of Questions “Does…” “What…” “How…” “How…”

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Examples of Approaches in Each Epistemology

Objectivism

  • Postivism
  • Post-positivism

Constructionism

  • Phenomenology
  • Hermenuetics
  • Ethnography

Subjectivism

  • Post-modernist
  • Structuralist
  • Post-structuralist
  • Critical Inquiry

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Some Objectivist Research Approaches

Postivism

  • An independent reality exists and we can learn about it through empirical investigation.

Post-positivism

  • An independent reality exists but we can only know it indirectly. Big focus on the process of inference and warranting knowledge claims.

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Some Constructionist Research Approaches

Phenomenology

  • The study of lived experience from the perspective of those who experience it (Trying to make meaning of a phenomenon)

Hermenuetics

  • Interpretation of signs and symbols as systems of meanings as they are used (Trying to make meaning of a symbol system)

Ethnography

  • Study of how people in a group manage and organize their lives as social actors (Trying to make meaning of a culture)

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Some Subjectivist Research Approaches

Post-modernist

  • Research constructs, rather than reveals, meaning. Focus on subjectivity and plurality of meaning across (and within) participants and the researcher.

Structuralist & Post-structuralist

  • The study of how meaning is produced within a culture through its structures (linguistic, psychological, sociological). Post-structuralism does not assume critiques structuralist assumptions that structures are unitary, uncontentious and timeless

Critical Inquiry & Feminist

  • Driven by a concern with existing power inequalities, actively promotes social change, often begins begins with the standpoints and experiences of the less powerful

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The Research Proposal

Research Management Process

Phase 1: Research Proposal

Phase 2: Research Plan

Phase 3: Research Execution

Phase 4: Dissertation/thesis/report

Phase 5: Examination and Dissemination

NB Refer to Document: Nap_ research proposal_guide_2014123

Subject matter of lecture: The Research Proposal

After examining the processes of selecting the research topic, in this lecture we examine in detail the research proposal:

__the purpose;

b. __ the content of the research proposal -- title or topic – background which includes the problem statement and relevant literature or problem statement – literature review – research objectives/questions/hypothesis – method/procedure – time scale – resources – references – research budget.


The Research Proposal

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Subject matter of lecture: The Research Proposal

Length

Your Research Proposal should aim to be clear and concise but will vary in length according to the subject area:

For Humanities, Languages, Education, Social Sciences and Business Related studies, research proposals should be in the region of 2,500 - 3,750 words

Science, Computing and Engineering research proposals should be in the region of 1000 - 1500 words


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Subject matter of lecture: The Research Proposal

Not a commitment

The proposal you submit at this stage will not commit you to an irrevocable course of research, rather it will allow us to assess the potential of the project, and whether we have sufficient expertise in the area to provide adequate supervision for your project.

It will also serve as an indicator of your ability to undertake a masters/doctoral studies



The Research Proposal

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Subject matter of lecture: The Research Proposal

Cover

-- the title of the proposal

A good title is usually a compromise between conciseness and explicitness. Although titles should be comprehensive enough to indicate the nature of the proposed work, they should also be brief.


The Research Proposal

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Subject matter of lecture: The Research Proposal

Cover

-- the title of the proposal

-- Name : your name

-- Level of study/degree : Master Research Proposal

-- Institution : the name and address of the University/unit

-- Date : date submitted



The Research Proposal

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Subject matter of lecture: The Research Proposal

Abstract

Every proposal, even very brief ones, should have an abstract.

Some readers read only the abstract, and most readers rely on it initially to give them a quick overview of the proposal and later to refresh their memory of its main points.


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Subject matter of lecture: The Research Proposal

Abstract

Though it appears first, the abstract should be written last, as a concise summary (approximately 200 words) of the proposal.

It should appear on a page by itself numbered with a small Roman numeral if the proposal has a table of contents and with an Arabic number if it does not.



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Subject matter of lecture: The Research Proposal

Table of Content

Long and detailed proposals may require, in addition to a table of contents, a list of illustrations (or figures) and a list of tables.

If all of these are included, they should follow the order mentioned, and each should be numbered with lower-case Roman numerals. If they are brief, more than one can be put on a single page.

The table of contents should list all major parts and divisions (including the abstract, even though it precedes the table of contents). Subdivisions usually need not be listed. Again, the convenience of the reader should be the guiding consideration.


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Subject matter of lecture: The Research Proposal

Chapter One

Introduction

The introduction of a proposal should begin with a capsule statement of what is being proposed and then should proceed to introduce the subject to a stranger.

It should give enough background to enable him to place your particular research problem in a context of common knowledge and should show how its solution will advance the field or be important for some other work.


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Subject matter of lecture: The Research Proposal

Chapter One

Background

Sufficient details should be given in this section (1) to make clear what the research problem is and exactly what has been accomplished; (2) to give evidence of your own competence in the field; and (3) to show why the previous work needs to be continued. Some sponsors want to know also who has funded the previous work.

Discussions of work done by others should therefore lead the reader to a clear impression of how you will be building upon what has already been done and how your work differs from theirs. It is important to establish what is original in your approach, what circumstances have changed since related work was done, or what is unique about the time and place of the proposed research.


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Subject matter of lecture: The Research Proposal

Chapter One

Statement of the Research Problem

Research forms a cycle.  It starts with a problem and ends with a solution to the problem.  The problem statement is therefore the axis which the whole research revolves around, because it explains in short the aim of the research.


The Research Proposal

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Subject matter of lecture: The Research Proposal

Chapter One

Statement of the Research Problem

What is a Research Problem __source?

Own experience or the experience of others may be a source of problem supply. 

scientific literature/policy/ies.

Theory/ies


The Research Proposal

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Subject matter of lecture: The Research Proposal

Chapter One

Statement of the Research Problem

Sub-problem (s)

Sub-problems are problems related  to the main problem identified.   Sub-problems flow from the main problem and make up the main problem. 

It is the means to reach the set goal in a manageable way and contribute to solving the problem.


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Subject matter of lecture: The Research Proposal

Chapter One

Statement of the Research Problem

What is a research problem? 

A research problem is  the situation that causes the researcher to feel apprehensive, confused and ill at ease. 

It is the demarcation of a problem area within a certain context involving the WHO or WHAT, the WHERE, the WHEN and the WHY of the problem situation.


The Research Proposal

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Subject matter of lecture: The Research Proposal

Chapter One

Statement of the Research Problem

Keep the following in mind:

  • Highlight key theories, concepts and ideas current in this area.
  • Why are these issues identified important?
  • What needs to be solved?


The Research Proposal

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Subject matter of lecture: The Research Proposal

Chapter One

Statement of the Research Problem

CHECKLIST FOR TESTING THE FEASIBILITY OF THE RESEARCH PROBLEM:

  • Is the research free of any ethical problems and limitations?

  • Is the research problem important?  Will you be proud of the result?


The Research Proposal

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Subject matter of lecture: The Research Proposal

Chapter One

Research Goals and Objectives

Goal __ broad purpose of study

The primary goal of this research project is to standardize the measurement and characterization of building energy performance. Outcomes of this project include a common language and standards that produce consistent results independent of the user.


The Research Proposal

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Subject matter of lecture: The Research Proposal

Chapter One

Research Goals and Objectives

Objectives __ secondary purpose/aims of study

Although many performance attributes of buildings require clearly defined metrics, we are only concerned with metrics related to energy consumption and on-site energy production. Our objectives are to:


The Research Proposal

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Subject matter of lecture: The Research Proposal

Chapter One

Research Goals and Objectives

Objectives __ secondary purpose/aims of study

More specifically, the objectives of this study are as follows:

Identify existing performance metrics

Clearly define new performance metrics that characterize building energy performance and provide guidance on their use

Standardize the methods or procedures for quantifying the metrics

Develop and validate procedures for collecting data, assuring data quality, and reporting data needed to support the building energy performance metrics.


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Subject matter of lecture: The Research Proposal

Chapter One

Study Assumption(s)

Philosophical work abounds on the use of assumptions in both natural science and social science.

assumptions are integral parts of scientific explanations


The Research Proposal

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Subject matter of lecture: The Research Proposal

Chapter One

Hypotheses

An hypothesis is a specific statement of prediction. It describes in concrete (rather than theoretical) terms what you expect will happen in your study.

Not all studies have hypotheses.

A single study may have one or many hypotheses.


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Subject matter of lecture: The Research Proposal

Chapter One

Hypotheses

Alternative /null hypothesis

Usually, we call the hypothesis that you support (your prediction) the alternative hypothesis, and we call the hypothesis that describes the remaining possible outcomes the null hypothesis.


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Subject matter of lecture: The Research Proposal

Chapter One

Hypotheses

Sometimes we use a notation like HA or H1 to represent the alternative hypothesis or your prediction, and HO or H2 to represent the null case. You have to be careful here, though.

Alternative hypothesis __ HA or H1

Null hypothesis __ HO or H2


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Subject matter of lecture: The Research Proposal

Chapter One

Hypotheses

Working example : Alternative /null hypothesis

For instance, let's imagine that you are investigating the effects of a new employee training program and that you believe one of the outcomes will be that there will be less employee absenteeism. Your two hypotheses might be stated something like this:


The Research Proposal

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Subject matter of lecture: The Research Proposal

Chapter One

Hypotheses

Working example : Alternative /null hypothesis

The null hypothesis for this study is:

HO: As a result of the XYZ company employee training program, there will either be no significant difference in employee absenteeism or there will be a significant increase.

which is tested against the alternative hypothesis:

HA: As a result of the XYZ company employee training program, there will be a significant decrease in employee absenteeism.


The Research Proposal

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Subject matter of lecture: The Research Proposal

Chapter One

Hypotheses

keep in mind: Alternative /null hypothesis

The important thing to remember about stating hypotheses is that you formulate your prediction (directional or not), and then you formulate a second hypothesis that is mutually exclusive of the first and incorporates all possible alternative outcomes for that case.


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Subject matter of lecture: The Research Proposal

Chapter One

Hypotheses

keep in mind: Alternative /null hypothesis

If your prediction was correct, then you would (usually) reject the null hypothesis and accept the alternative.

If your original prediction was not supported in the data, then you will accept the null hypothesis and reject the alternative.


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Subject matter of lecture: The Research Proposal

Chapter One

Hypotheses

Working example: Environmental policy, innovation and performance : new insights on the Porter hypothesis

Jaffe and Palmer (1997) present three distinct variants of the so-called Porter Hypothesis.

The “weak” version of the hypothesis posits that environmental regulation will stimulate certain kinds of environmental innovations.

The “narrow” version of the hypothesis asserts that flexible environmental policy regimes give firms greater incentive to innovate than prescriptive regulations, such as technology-based standards.


The Research Proposal

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Subject matter of lecture: The Research Proposal

Chapter One

Hypotheses

Working example:

Finally, the “strong” version posits that properly designed regulation may induce cost-saving innovation that more than compensates for the cost of compliance.

In this paper, we test the significance of these different variants of the Porter Hypothesis using data on the four main elements of the hypothesized causality chain (environmental policy, research and development, environmental performance and commercial performance).


The Research Proposal

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Subject matter of lecture: The Research Proposal

Chapter One

Hypotheses

Working example:

The analysis is based upon a unique database which includes observations from approximately 4200 facilities in seven OECD countries. In general, we find strong support for the “weak” version, qualified support for the “narrow” version, and qualified support for the “strong” version as well.


The Research Proposal

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Subject matter of lecture: The Research Proposal

Chapter One

Methodology of the Study

the science of method; the science dealing with principles of procedure in research and study.

--- Research like any other socially constructed activity exists in context

--- Hence, the need to state the particular method/s of collecting and collating data and information


The Research Proposal

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Subject matter of lecture: The Research Proposal

Chapter One

Significance/Justification of the Study

Critical analysis of the concept/paradigm

Knowledge __ reflexive consideration of the implications of the research for knowledge production

Policy: __ policy formulation __ solve a problem


The Research Proposal

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Subject matter of lecture: The Research Proposal

Chapter One

Scope of the Study

Para-meters of study

Each study has its own Parameters __ depth of study (micro/macro) or meso


The Research Proposal

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Subject matter of lecture: The Research Proposal

Chapter One

Limitation/s

Anticipated problems

Time/Financial

Biases of various administrators of questionnaire

Socio-economic and political


The Research Proposal

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Subject matter of lecture: The Research Proposal

Chapter One

Organisation of the study

Chapters

---- Division of the dissertation/report into chapters (Five/Six)

---- Summary of each chapter; linkage


The Research Proposal

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Subject matter of lecture: The Research Proposal

Chapter Two

Literature Review

What is a literature review?

It is a critical look at what has been written on a topic by accredited scholars and researchers.

It is NOT just a summary of other people's work.


The Research Proposal

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Subject matter of lecture: The Research Proposal

Chapter Two

Literature Review

What is its purpose?

You are showing your ability to scan the literature efficiently and apply principles of analysis to identify unbiased and valid studies.

You are demonstrating your ability to synthesise information and think critically.


The Research Proposal

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Steps to a Lit Review

Problem formulation:

  • which topic or field is being examined and what are its component issues?

Literature search:

  • Find materials relevant to the subject being explored

Data evaluation:

  • determine which literature makes a significant contribution to the understanding of the topic

Analysis and interpretation:

  • discuss the findings and conclusions of pertinent literature


The Research Proposal

Source: http://writingcenter.utah.edu/_docs/organization_693_1320713252.pdf

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The Purpose of a Literature Review

To explain the historical background of a topic

To describe and compare the schools of thought on an issue

To synthesize the available research

To highlight and critique research methods

To note areas of disagreement

To highlight gaps in the existing research

To justify the topic you plan to investigate


The Research Proposal

Source: http://writingcenter.utah.edu/_docs/organization_693_1320713252.pdf

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Common Problems

DO NOT:

Include every source found

Include every source in a sequential order

Summarize without relating the source to the topic

Organize the discussion in an ineffective manner

Lose track of sources and spend time searching for them.


The Research Proposal

Source:

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Transitions

Transitions help connect paragraphs together

Examples:

One of the first researchers to investigate this problem is Chen . . .

Smith and Jones counter Chen’s argument . . .

The issue becomes more complex when a third school of thought is considered . . .

One researcher who agrees with Chen is . . .

A different approach to this question looks at problems in X

One of the most troublesome problems is addressed by Green . . .

A problem with this approach is that . . .

A recent study adds this to the mix . . .

A crucial issue that has not been addressed is z . . .

Adapted from the University of Houston-Clear Lake PowerPoint


The Research Proposal

Source:

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Tips for Success

Look at other lit reviews in your area of interest or in the discipline

Clarify the assignment with your instructor

Keep track of sources

Give yourself time for multiple drafts

Have someone in your field read your lit. review


The Research Proposal

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Example

Most of the professional and scholarly literature on downtown development has

neglected small cities. Frieden and Sagalyn's (1999) widely cited book Downtown, Inc.

concentrates on largescale projects in Seattle, Boston, St. Paul, and San Diego, while

Loukaitou-Sideris and Banerjee (1998) profile Los Angeles, San Francisco, and San Diego in

their book on downtown design.

Almost all the examples provided in Whyte (1988), Abbott (1993), and Robertson (1995)

are from large cities, and Brooks and Young (1993) use New Orleans as their case study. The

Downtown Development Handbook (McBee, 1992), considered by many to be the bible of

downtown development, is heavily dependent on projects in large cities to illustrate key

points.

Articles addressing a particular downtown development strategy such as retailing

(Robertson, 1997; Sawicki, 1989), stadiums (Noll and Zimbalist, 1997; Rosentraub, Swindell,

Pryzbylski, and Mullins, 1994), pedestrianization (Byers, 1998; Robertson, 1993), and open

space (Loukaitou-Sideris, 1993; Mozingo, 1989) all emphasize large cities as well. The

professional magazine Urban Land has published numerous articles on downtown

development in recent years, most of which feature a single large city (e.g., Holt, 1998;

Howland, 1998; Lockwood, 1996)


The Research Proposal

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Subject matter of lecture: The Research Proposal

Chapter Two

Literature Review

Parts of literature Review

you should view it as having three main sections:

  • An introductory section which sets the context for the reader

  • The main body where you develop your argument and discuss the literature

  • A conclusion. 


The Research Proposal

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Recap __ What is a Literature Review

A Lit Review is NOT

a report that summarizes articles and books about many different topics

a research paper

a list of important research, presented chronologically (in most cases)

A Lit Review

surveys scholarly articles, books, and journals relevant to your narrow topic.

provides a description, summary, and critical evaluation of each scholarly work.

provides an overview of the significant literature published on your topic

http://writingcenter.utah.edu/_docs/organization_693_1320713252.pdf

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Recap _ Evaluate the Literature

How do you know what literature to include?

Possibly most recent information

Questions to consider:

Has the author clearly defined the problem/issue?

Could the problem have been approached more effectively from a different perspective?

Does the author show bias?

What is the author’s theoretical approach?

How good is the study design?

How valid are the results?

Are there flaws in the logic of the discussion?

How does the work contribute to the discipline’s understanding of the problem?

What problems has the author avoided or ignored?

Recap _ Evaluate the Literature (Con’t)

Evaluating and describing

other people’s work

  • What is a literature review?
  • How do I decide what goes where?
  • Learning From Secondary Research
  • Evaluating Primary Research
  • Additional information

1: What is a literature review?

What is a literature review?

  • A description of your topic area, supported by references

  • A summary, discussion and critical analysis of academic work related to your research question

What can you gain from literature reviews?

  • Ideas about which approaches are likely to work, and which are the best

  • If you find something similar, you can get
  • Ideas for how to implement your deliverable
  • Ideas for how to evaluate your deliverable
  • The best ways to do things
  • E.g. the fastest search algorithm
  • Justification for the approach that you are taking

Copying

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automatic fail

What can you gain from literature reviews?

  • Knowledge of what everyone else has done so that you don’t exactly replicate it

  • Without a literature review you risk reinventing the wheel
  • If you find something similar you may have to slightly change what you are doing to make it novel, or build upon what you found

Organising the review

  • In a separate chapter
  • The normal approach, used in this module

  • In several chapters
  • If it is the main part of the thesis, or very important
  • Incorporated throughout the thesis as and when needed
  • In addition to either of the other two approaches
  • Perhaps incorporating individual facts from books

May use books as a reference source: e.g. the definition of SSL.

Overall Structure [Very important]

In this module, we insist on a 3 part approach

  • Part 1: Overview of your chosen broad topic
  • The field that contains your research question

  • Part 2: Explanation of your sub-area
  • The area containing your research question

  • Part 3: In-depth analysis of research relevant to your research question
  • Critical evaluation of the primary research papers directly related to your research question

Overall Structure

Broad Topic

Specialist sub-area

Relevant

Primary

research

Your research question

Add your research topics

Melding the Structure

The three parts of the review need to be melded together by explaining:

  • how part 2 fits inside part 1
  • how part 3 fits inside part 2

2. How do I decide what goes where?

Conceptual models to help understanding

  • Conceptual models can help you understand how the different research fits together
  • Helps you decide upon an appropriate structure

  • Choose a technique that you are comfortable with
  • Spider diagram of the different papers?
  • List of keywords?

The literature review process

  • The literature review process should help shape
  • what you do, and
  • how you do it

You should be prepared to change your mind or alter your approach in response to what you discover

Always write up the literature review soon after reading the papers – otherwise you may forget what you have read

The finished review should also shape how others perceive the quality/value of your work

The literature review process

The first role of

a literature review

is often to learn

the topic.

3: Learning From Secondary Research

Secondary research includes books, research summaries and literature reviews

Secondary Research

  • Books, literature reviews and web pages can give an overview of a research area

  • These can help you to learn what a research area is about before having to read primary research

  • Literature reviews should always start with reading secondary research, if possible

Copying

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automatic fail

Reading books

  • Not enough time to read many books all the way through
  • Can still use as a reference source, just read relevant chapters/sections

  • Books tend to be less controversial and may also summarise the pros and cons of topics discussed (i.e. like a mini-literature review)

  • The introduction of a relevant book should summarize the research area

4: Evaluating Primary Research

Primary research is research papers containing new findings - not a literature review

Copying

=

automatic fail

Your evaluation 1

  • Read the abstract and conclusions first
  • Is it really relevant and useful to you?
  • Summarise how it is useful

  • Read the whole paper
  • Are the methods correct?
  • [BOUNDARIES] Is the scope of the study appropriate for your use?
  • Make sure that you understand the findings

  • [CONCLUSIONS] Read the abstract and conclusions again
  • To make sure that you have understood the results and their context

Your evaluation 2

  • [LIMITATIONS] The discussion of limitations
  • Should be near the end of the paper
  • Read to make sure that the findings really apply to the situation in which you are using them

  • How significant is the work?
  • Does it make a major contribution?
  • How does it complement other papers?

BOUNDARIES, LIMITATIONS, CONCLUSIONS

BOUNDARIES, LIMITATIONS, CONCLUSIONS are the key ideas here

Multiple Papers

  • Need more than one paper about important points
  • Discuss points of agreement
  • Discuss points of difference
  • In computing there can be differences of opinion, but should not be big differences of fact
  • The papers may try different techniques to solve the same problem
  • Must use your own opinion on differences, but try to argue your case

Copying

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automatic fail

Selective reading

  • What to do if you do not understand the paper
  • Too complex maths
  • Too much terminology
  • >>Consider reviewing the outcomes of the research and not the technical details [black box approach]

  • When to just read the abstract
  • If it is clear that the paper is not relevant.
  • Can still cite the paper in the literature review if it is near to being relevant and explain briefly why it is not relevant
  • This shows that you have done a better literature review

Maths – have to skip, in refereed publications, should be able to take it on trust that the referees have checked it, unless there are other reasons to suspect it.

Get other papers, do web search for terminology.

Common mistakes 1

  • Your own opinions without backup, no matter how strongly you believe them (it does not count!)
  • E.g. Microsoft is good/rubbish

  • Discussing irrelevant literature
  • Short literature reviews
  • Have not demonstrated the literature review skill
  • Have not shown that you understand the context of your work
  • Not targeting the review at an appropriate audience

Common mistakes 2

  • Not joining the literature together into a coherent whole

  • Not targeting the review to the research question
  • Missing an important reference
  • Your work is seriously undermined if you write about something as though you are the first, but someone else has already published on the subject

Swales’ perspective

  • "not sufficiently theme-based“

  • "not structured according to the issues“

  • "insufficiently informed by the research hypotheses“

  • "boringly chronological"

  • "just describe each piece of research one by one without adequate linkage"

Professor John Swales, linguistics expert on the structure of scientific communication.

5: Additional information

Plagiarism

Plagiarism is passing off somebody else’s work as your own

  • In a literature review you are discussing other people’s work so must clearly reference it

  • In this module you must NEVER copy an entire phrase or sentence even if it is in quotes and properly referenced
  • If you cannot describe something in your own words then you must not include it in your literature review.

  • If you paraphrase other people’s ideas you still need to reference them

Writing tips

  • Structure to present an argument, discussing similar papers together
  • Do not list the papers and write separate reviews

  • Explain the useful information that each paper gives and compare and contrast to other similar papers

  • Build an argument that will support the main aim of your thesis
  • What you are doing/How you are going to do it/Why you are doing it
  • Show how what you are doing is different to what has been done before

Referencing your work

  • You MUST reference your work

  • This means giving the author name and publication date for all ideas and facts you have used (Harvard /APA format)

Failure to reference your work is Plagiarism and is an automatic fail.

Annotated Bibliography

Session Vocabulary

You might also hear or see

Annotated bib

or

Annotation

or

Lit review

All of these terms are synonymous with annotated bibliographies or literature reviews.

Do note that throughout the presentation, and within your coursework, you may hear terms like annotated bib, annotation, or lit review. [CLICK] Know that annotated bib and annotation are other ways to refer to elements in an annotated bibliography, and lit review is a shortened way to say literature review.

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Annotated Bibliographies

What does the term mean?

According to Merriam-Webster’s Online Dictionary (2011),

Annotate: “to make or furnish critical or explanatory notes or comment”

Bibliography: “the history, identification, or description of writings or publications”

How do these definitions combine?

Now that we have some vocabulary terms settled, let’s first begin with the term annotated bibliography. We at the Writing Center often receive questions from students on what this term means, so let’s break it down: According to Merriam-Webster’s Online Dictionary, the verb “annotate” means “to make or furnish critical or explanatory notes or comment.” The words to pick out of this definition are “critical, explanatory, and notes.” As for the term bibliography, this term is defined as “the history, identification, or description of writings or publications.” So, if we are to combine these terms together, we can determine that an annotated bibliography is a collection of explanatory, critical notes on a list of sources. You could also think of this term to refer to a reference list with a chunk of text below each entry that describes the nature of that source.

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Annotated Bibliographies

The purpose of an annotation bibliography is:

  • To learn about a particular topic

  • To demonstrate the value of a particular source

  • To inform fellow or future researchers about a topic or a source

Before we get into how to create an annotated bib, writers do need to understand the purpose of the assignment. Without knowing why you are writing an annotated bib, your assignment might not be fulfilling the expectations of your instructor. Overall, there can be many functions of an annotated bib. As a reader, you might want to seek out an annotated bibliography to learn about a specific topic. As a writer, however, creating a annotated bib will allow you to demonstrate the value (or lack of value) of a particular source and to help inform future researchers about a source or topic.

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Annotated Bibliographies

The format of an annotated bibliography can change depending on the assignment, but the typical format is a list of reference entries followed by annotations.

  • Alphabetized by author
  • No headings
  • Brief

One of the primary questions we at the Writing Center receive about annotated bibliographies is how to format them. Understanding the format of an annotated bib can be the first step in your prewriting process. Essentially, while the format can change depending your specific assignment, an annotated bibliography is formatted as a list of alphabetized reference entries (think of how a typical course paper reference list would look), with each entry followed by an annotation. There are typically no headings to separate the sources or within the annotations, and each annotation should be brief (anywhere from one to two pages). [CLICK] While these formatting requirements are typical for annotated bibs, be sure to ask your instructor about any alternative expectations for your specific assignment.

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Annotated Bibliographies

Here is a visual representation of a portion of an annotated bibliography. [CLICK] You will notice the annotation begins with a reference citation, followed immediately by the first line of text. This reference citation should be in typical APA formatting (for example, double spaced, using a hanging indent, and so forth). This first annotation is concise and is only about one page in length. [CLICK] Typically, you will want subsequent annotations to begin immediately following the previous one. Note that there are no spaces or headings between the end of this first annotation and the new reference entry of the next.

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Annotated Bibliographies

Within each annotation, there are typically three elements:

  • Summary
  • Critique/analysis
  • Application

These elements can often be formatted as three paragraphs.

Now that we have the overall formatting down, let’s get into the nitty gritty details about what an “annotation” is and what is entailed within this text that follows the reference entry. As you begin to construct your annotation, you will focus on three elements: a summary of the source, a critical analysis of the source, and an explanation of how that source applies to your particular topic. To ensure that you fully develop each part of the annotation, instructors will usually ask for each element to be in its own paragraph.

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Annotated Bibliographies

Summary

You will want to answer some or all the following questions:

  • What is the topic of the source?
  • What actions did the author perform within the study and why?
  • What were the methods of the author?
  • What was the theoretical basis for the study?
  • What were the conclusions of the study?

Let’s start with the summary element. After reading a source and determining how it would fit into your research or topic, your natural instinct is to, typically, summarize the source. When creating a summary paragraph for an annotation, some questions to answer and include within your summary paragraph could be:

What is the topic of the source?

What actions did the author perform within the study and why?

What were the methods of the author?

What was the theoretical basis for the study?

What were the conclusions of the study?

These questions hit at all key elements of a study and give your reader a high level view of that source.

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Annotated Bibliographies

How to approach a summary:

  • Similar to an abstract of a source
  • In the past tense

“The authors found…”

  • Not the abstract of an article
  • Should be written in your own words

Just like answering those questions, some strategies to creating a summary paragraph are to think of it like an abstract, which introduces the topic, development, and conclusions of an article. However, you will want your phrasing of the summary to be in the past tense per APA 3.06 preferences, using phrases like “The authors found…” or “stated.” Do note, though, that a summary paragraph should not be the exact abstract of the article. Avoid the temptation to copy/paste the abstract information into an annotation and instead summarize the source in your own words.

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Annotated Bibliographies

Example Summary:

Gathman, A. C., & Nessan, C. L. (1997). Fowler’s stages of faith development in an honors science-and-religion seminar. Zygon, 32(3), 407–414. Retrieved from http://www .zygonjournal.org/

The authors described the construction and rationale of an honors course in science and religion that was pedagogically based on Lawson’s learning cycle model. In Lawson’s model, the student writes a short paper on a subject before a presentation of the material and then writes a longer paper reevaluating and supporting his or her views. Using content analysis, the authors compared the answers in the first and second essays, evaluating them based on Fowler’s stages of development. Examples of student writing are presented with the authors’ analysis of the faith stage exhibited by the students, which demonstrated development in stages 2 through 5.

Let’s take a look at this sample summary paragraph of an annotation. Note: these elements, like the reference entry and text, will be double spaced for the final paper. I won’t read this entire paragraph to you, but I’ll quickly highlight what the author is doing here. [CLICK] First, this opening line of the annotation immediately discuss the topic and purpose of the article. The reader does not have to dig through a lot of background information to get to the “meat” of the summary. [CLICK] Also, this writer briefly refers to the method, data collection, and analysis of the material that these authors included in the article. This student included all of these elements into one or two sentences, which allows the reader to quickly move into the conclusions of the article. [CLICK] These conclusions are also mentioned in this summary paragraph at the end, and this student takes particular care to mention the results of the study and their overall implication to the study.

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Annotated Bibliographies

Critique/Analysis

You will want to answer some or all the following questions:

  • What are the strengths and weaknesses of the article?
  • Methodology, language choices, organization, level of detail
  • What, if any, information is missing?
  • Is the article scholarly or generalizable? Why or why not?

The next element to a successful annotation is the critique or analysis portion. This aspect is often neglected by students, but this paragraph can be the most important to you as the researcher and to your reader. To help create this paragraph, try answering the following questions:

What are the strengths and weaknesses of the article?

Methodology, language choices, organization, level of detail

What, if any, information is missing?

Is the article scholarly or generalizable? Why or why not?

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Annotated Bibliographies

How to approach a critique/analysis:

  • Focus on strengths of the article or study
  • What would make your reader want to read this source?

Do not feel the need to be nice

  • Your reader will want to know if there are any deficiencies or areas for improvement

There are also some ways to approach your critique outside of these questions, and the best way to ensure you don’t forget to include this part to the annotation is to focus on the strengths. Highlighting these strengths will help an outside reader understand the impact and influence this source has on your research field, and this approach can also help you remember to revisit this source as you develop your own study and want to know what works best. However, students often will feel the need to be “nice” to the author in the analysis paragraph just because the article has been peer reviewed or published. Remember, though, that the majority of published authors in the social science field were once students, and just like a capstone or final project for a course, there can also be room for revision or areas for improvement. If you wished that the author had place more emphasis on a particular result or included more tables in the article to aid readability, feel free to refer to these missing elements and explain how they could have improved the source. That way, your reader will know that you not only engaged with the topic of the article but also the method and mode of the written aspect.

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Annotated Bibliographies

Example Critique/Analysis:

The authors made no mention of how to support spiritual development in the course. They were interested in the interface between religion and science, teaching material on ways of knowing, creation myths, evolutionary theory, and ethics. They exposed students to Fowler’s ideas, but did not relate the faith development theory to student work in the classroom. There appears to have been no effort to modify the course content based on the predominant stage of development, and it is probably a credit to their teaching that they were able to conduct such a course with such diversity in student faith development. However, since Fowler’s work is based largely within a Western Christian setting, some attention to differences in faith among class members would have been a useful addition to the study. There was no correlation between grades and level of faith development.

Using this approach, here’s an example analysis paragraph in an annotated bibliography. [CLICK] The student here notes an aspect of the article where information was lacking. Similarly, [CLICK] at the end of the paragraph, the student mentions what could have been added or improved in the study but is being constructive in the approach.

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Annotated Bibliographies

Application

You will want to answer some or all the following questions:

  • Does this article fill a gap in literature?
  • How would you be able to apply this method to your area of focus?
  • Is the article universal?

The final element to an annotation is the application portion. This element can be just as tricky as the analysis part, as it requires you as the writer to view a source not just by methods or written quality but as a piece of literature in the broader field of research. To do so, you will want to answer these types of questions:

Does this article fill a gap in literature?

How would you be able to apply this method to your area of focus?

Is the article universal?

Don’t feel any pressure to “get it right,” though. Remember this annotation is your interpretation of the applicability of a source, so as long as you have support to back up your claims, your reader will understand your rationale for this annotation element.

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Annotated Bibliographies

How to approach the application:

  • Consider how you would justify the use of the source for your paper
  • How is this source different than others in the same field or on the same topic?
  • How does this source inform your future research?

When creating the application paragraph, which is often the shortest paragraph in an annotation, consider how you would justify using (or not using) a particular source for your research. Focusing on the unique elements of a source, such as population or method, will help you collect a series of diverse sources on your topic. Similarly, though, if a source is too unique or too narrow, include these limitations in your text. In addition, this application portion should hint at how this source would justify the need for your own research, such as if an author mentioned how to build upon a study or where the field as a whole needs more data.

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Annotated Bibliographies

Example Application:

Fowler’s work would seem to lend itself to research of this sort, but this model is the only example found in recent literature. This study demonstrates the best use of the model, which is assessment. While the theory claimed high predictive ability, the change process chronicled is so slow and idiosyncratic that it would be difficult to design and implement research that had as its goal measurement of movement in faith development continuum.

These three elements create an annotation.

Let’s take a look at this example application paragraph. You may notice that, when compared to the previous two paragraphs, it is the shortest. [CLICK] here, the student mentions how this article and approach is unique within the literature. [CLICK] Also, the student ends with a discussion on the universal nature of the source and why (or why not) it would be beneficial to the student’s own research or topic. [CLICK] These three elements, summary, critique, and application, create an “annotation.”

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Annotated Bibliographies

There are some unique characteristics of which to be aware regarding annotations:

  • No citations crediting the source or outside sources
  • The presence of the reference entry make citation redundant
  • No direct quotes
  • Your reader will want to hear your interpretation of the material
  • No referrals to the first person
  • Be objective and removed in your description of the source
  • No reference list
  • All sources have already been included in the reference list format

Again, be sure to contact your instructor for his/her expectations for your particular assignment.

When constructing these annotations, do know that there are some unique characteristics of which to be aware, and these characteristics do differ from typically coursework expectations. First, there is no need to cite your source within your annotation; the reference entry that begins the annotation will let your reader know to what source you are referring (which, in essence, is what a citation is supposed to do). Also, you will want to avoid including any outside source citations. Each annotation should purely focus on what is housed within that source, and comparing/contrasting sources should be saved for a literature review (which we’ll get to soon). Second, direct quotes should not be present in an annotation. This text is intended for you as the author to demonstrate the value of a source, so paraphrasing is key. You will also want avoid references to yourself or the first person (like I, my, or mine). An annotation should be objective and, just like the guideline to only paraphrase, should just focus on the validity of the source in regard to the overall field. And lastly, you will typically not have to include a reference list for an annotated bibliography, as each source has already been included in its APA format. However, if an annotated bibliography is a part of a longer document, there may be different requirements for that assignment, so contact your instructor for his or her preferences.

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Annotations and Lit Reviews

How does an annotation relate to a literature review?

An annotated bibliography is often a precursor to a literature review, as it allows an author to collect sources and determine their value to a particular topic or area of research.

In a literature review, the author uses the sources to create a foundation for his/her research.

Now that we know the purpose and format of an annotated bibliography, how does this assignment relate to a literature review? [CLICK] Well, an annotated bibliography is often the first step to creating a literature review, as it will allow you to collect sources and determine their value to your research. [CLICK] In a lit review, however, you will use these sources together to create a foundation or justification for your research.

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REFERENCING STYLES

American Psychological Association (APA Style)

Harvard Referencing Style

Your Guide to
the Magic and Mystery of
APA Style

Why do you have to do this?

Because learning to write means mastering an accepted and uniform writing style.

Because APA style is the most common writing style in the social sciences.

What’s Included in APA Style?

Basically everything in your paper:

- How your pages are set up

- How you cite sources

- Your references

- Even your language

We’ll start with the list of references

Required if you cite any sources in your paper

Every source cited in your paper must appear on the reference list, and every entry in your reference list must be cited in your paper

Double spaced!

Single-authored book

Perloff, R. M. (1995). The dynamics of persuasion. Hillsdale, NJ: Erlbaum.

*Note: In the 5th edition of APA, there is NO underlining

Reissued book

Newcomb, H. (Ed.). (1995). Television: The critical view (5th ed.). New York: Oxford University Press.

*Note: Capitals in the title of the book are restricted to the first letter of the first word of the title, the first letter of any proper names, and the first letter of the first word after a semicolon, period, or question mark.

Dual-authored book

Baran, S. J., & Davis, D. K. (1995). Mass communication theory: Foundations, ferment and future. Belmont, CA: Wadsworth.

*Note: when listing authors, use an ampersand (&) in the reference list, not “and.”

Essay or chapter in an edited book

Bryant, J. (1989). Message features and entertainment effects. in J. J. Bradac (Ed.), Message effects in communication sceince (pp. 231-262). Newbury Park, CA: Sage.

*Note: You must include the page numbers if you’re just referencing one part of a book.

Single-authored article

Garramone, G. M. (1985). Effects of negative political advertising: The roles of sponsor and rebuttal. Journal of Broadcasting & Electronic Media, 29, 149-159.

*Note: The first letter of every important word in the title of the journal is capitalized.

Two or more authors (article)

Suzuki, S., & Rancer, A. S. (1994). Argumentativeness and verbal aggressiveness: Testing for conceptual and measurement equivalence across cultures. Communication Monographs, 61, 256-279.

*Note: Can you find the volume number and page numbers in this citation?

Unpublished convention paper

Thomas, S., & Gitlin, T. (1993, May). Who says there’s a dominant ideology and what happens if that concept is falsified? Paper presented at the annual meeting of the International Communication Association, Washington, DC.

*Note: Conference papers are less highly regarded than published works

Internet articles based on a print source

VandenBos, G., Knapp, S., & Doe, J. (2001). Role of reference elements in the selection of resources by psychology undergraduates. [Electronic version]. Journal of Bibliographic Research, 5, 117-123.

*Note: Sometimes electronic versions are different from the print versions.

Article in an internet-only journal

Frederickson, B. L. (2000, March 7). Cultivating positive emotions to optimize health and well-being. Prevention & Treatment, 3. Retrieved November 20, 2000, from http://journals.apa.org/prevention/volume3/pre0030001a.html

*Note: this would be the correct citation format for the article you abstracted.

Hang on, you’re not done!!

Learning how to do your reference page is only the beginning to APA style!!

When do you cite your sources in your work?

When you’re referring to an idea or concept you drew from something you read.

When you quote from something you read or heard.

When you want to give the reader some other places to look for additional information.

Paraphrasing

Scott (1992) identified…

Several researchers (Anthony, 1990; Gregory & Jacobs, 1985; Polk et al., 1980) reported…

Or at the end of a sentence paraphrased from another work (Scott, 1992).

Citing while paraphrasing

  • List the last names of all authors the first time you cite them, unless there are more than 5.

If there are more than five, or you are citing the paper of 3 or more authors for a second or more time, list last name of first author, followed by “et al.,” and the date.

Examples

Scott, Williamson, and Schaffer (1990) reported that…

(FIRST TIME)

Scott et al. (1990) reported that

(EVERY TIME AFTER)

Scott and Williamson (1990) reported that…

(FIRST TIME and EVERY TIME)

6 or more authors, use “et al.,” first time and every time.

Citing while quoting

You need to put the author last name(s) and date, like while paraphrasing, but also the PAGE NUMBERS or PARAGRAPH NUMBERS (for online sources).

Example: “the research findings clearly indicate support for the hypotheses” (Douglass, 1986, p. 55).

Warning

Keep quotations to a minimum (less than 3 per page)

  • Don’t forget the quotation marks and page numbers (or paragraph numbers), or you will be guilty of plagiarism!

How to set up your paper in APA

Double space EVERYTHING

Font should be pica 10 pitch or New Times Roman 12

Single spaces between sentences

Page numbers in upper right hand corners

Don’t get tied up in knots…
Ask your instructor if you’re unsure about anything..

Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design

Introduction section: Qualitative Research Methods

The section covers human inquiry and science, research paradigms, assumptions and types of qualitative studies (Case Study, Grounded theory, Phenomenology, Ethnography, Content Analysis and historical studies), Sampling and types of data collection that is interviewing, written descriptions and observations.


The Research Proposal

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Methodology and Methods

Methodology is not just method _ (Methodology and Method are often incorrectly used interchangeable)

The study of the general approach to inquiry in a given field

Methodology is the philosophical basis for methods:

Positivism; Phenomenology; Interpretative approaches

Method – the specific techniques, tools or procedures applied to achieve a given objective

  • Research methods in economics include regression analysis, mathematical analysis, operations research, surveys, data gathering, etc.

Methods of Collecting Data _Qualitative Versus Quantitative Methods

Qualitative assessment:

Collects data that does not lend itself to quantitative methods but rather to interpretive criteria; “data” or evidence are often representative words, pictures, descriptions, examples of artistic performance, etc.

Quantitative methods are associated with empirical, and positivist research .

Quantitative assessment:

Collects representative data that are numerical and lend themselves to numerical summary or statistical analysis.

Qualitative research is associated with anti-positive philosophies, such as inter-pretivism, ethnography, phenomenology, etc.

Programmess are free to select assessment methods appropriate to their discipline or service.... choices must be valid and reliable

Valid and Reliable Methods

Valid:

The method is appropriate to the academic discipline and measures what it is designed to measure

Reliable:

The method yields consistent data each time it is used and persons using the method are consistent in implementing the method and interpreting the data

Qualitative research...

Commonly called “interpretive research”

…its methods rely heavily on “thick” verbal descriptions of a particular social context being studied

  • Is useful for describing or answering questions about particular, localized occurrences or contexts and the perspectives of a participant group toward events, beliefs, or practices

…a helpful process for exploring a complex research area about which little is known

Generally speaking, qualitative researchers….

…spend a great deal of time in the settings being studied (fieldwork)

…rely on themselves as the main instrument of data collection (subjectivity; inter-subjectivity)

…analyze data using interpretative lenses

The general characteristics of qualitative research...

 Data are descriptive

 Emphasizes a holistic approach (processes and outcomes)

 Data sources are real-world situations

 Data analysis is inductive

 Describes the meaning(s) of research finding(s) from the perspective of the research participants

Uses inductive reasoning…

…involves developing generalizations from a limited number of specific observations or experiences

…highly dependent on the number and representativeness of the specific observations used to make the generalization

  • Issues in qualitative research...

b. contacting potential research participants

a. gaining entry

c. selecting participants

d. enhancing validity and reducing bias

e. leaving the field

a. gaining entry...

 may require considerable negotiation and compromise with a gatekeeper

 access is very much dependent upon the researcher’s personal characteristics and how others perceive the researcher

b. contacting participants...

 dealing with gatekeeper(s)

 gaining access

 issues of building trust and ensuring confidentiality and anonymity __ trust is earned, not given

c. selecting participants...

requires identifying participants who can provide information about the particular topic and setting being studied

 the goal is to get the deepest possible understanding of the setting being studied

Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design

Data Collection Methods__ methods of data collection to be used in the final study.

---- documentation

---- archival records

---- interviews/video recording

---- participant- observation/ direct observation

---- physical artefacts'

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design

Data Collection Methods__ methods of data collection to be used in the final study.

Secondary Data:

A secondary data research project involves the gathering and/or use of existing data for purposes other than those for which they were originally collected.

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design

Data Collection Methods__ methods of data collection to be used in the final study.

Sources of qualitative secondary data:

  • Biographies - subjective interpretation involved
  • Diaries - more spontaneous, less distorted by memory lapses
  • Memoirs - benefit/problem of hindsight
  • Letters - reveal interactions
  • Newspapers - public interest & opinion
  • Novels & Literature in general
  • Handbooks, Policy Statements, Planning Documents, Reports, Historical & Official Documents: Marx's use of Factory Inspectors reports in developing his theories of the labour process

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design

Data Collection Methods__ methods of data collection to be used in the final study.

Field Research (Direct Observation)

Participant observation: in-depth interviewing (building concepts and themes related to research problem)

---- in selected households, husbands, wives and any male or female eighteen years and above will be interviewed in either English or in the local language

Indirect Observation

Observation may be indirect, i.e. the researcher must rely on the reported observations (including self-observations of others).

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design

Data Collection Methods__ methods of data collection to be used in the final study.

Formal survey:

  • An important difference between quantitative surveys and informal methods is the rigidity of the procedure. A quantitative survey is carried out in a number of linear steps:

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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A quantitative survey is carried out in a number of linear steps:

  • Define variables
  • Identify indicators & values (operationalize)
  • Choose sampling procedure
  • Make sample frame and draw sample
  • Construct and pré-test questionnaire
  • Elaborate coding procedure
  • Prepare tables, charts to be used for analysis
  • Train interviewers
  • Inform the population
  • Train interviewers
  • Inform the population

Analysis:

  • Analyse and extrapolate frequency distribution, average, mean, mode, standard deviation, etc.
  • Carry out a statistical test of hypotheses (to establish the relationships between variables)

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Survey Research

Surveys are a systematic way of asking people to volunteer information about their attitudes, behaviors, opinions and beliefs. The success of survey research rests on how closely the answers that people give to survey questions matches reality that is, how people really think and act.

The first problem that a survey researcher has to tackle is how to design the survey so that it gets the right information.

Is this survey necessary?

Is the purpose of the survey to evaluate people or programs?

Can the data be obtained by other means?

What level of detail is required?

The second problem is how accurate does the survey have to be?

Is this a one-time survey or can the researcher repeat the survey on different occasions and in different settings?

How will the results be used?

How easy is it to do the survey?

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Use of Surveys by study design

Descriptive research

  • Describe phenomena and summarize them.

Causal explanation

  • Measure associations

Evaluation

  • Efficacy of a program

Prediction

  • Predict future events

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Basic Survey Designs

Cross-sectional surveys:

  • Data collected at one point in time selected to represent a larger population

Longitudinal Surveys:

Trend:

  • Surveys of sample population at different time points

Cohort:

  • Study of sample population each time data are collected but samples studied maybe different

Panel:

  • Data collection at various time points with the same sample of respondents

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Questionnaire – type of paper-and-pencil survey used in descriptive research in which information is obtained by asking participants to respond to questions rather than by observing their behavior

  • Limitation is that results are simply what people say they do, believe, like, dislike, etc.

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Questionnaire

Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design

Questionnaire Design

Open Ended

  • “Open” questions are those to which any answer can be accepted: e.g. how is the management of the irrigation scheme organized?

Closed Ended

  • “Closed” questions are those to which a definite answer is required: e.g. “How many rice varieties did you plant on your farm this year”.

Example __closed question

e.g.. do you use the rice variety introduced by the extension agency”

- yes 􀀀 no 􀀀


Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

*

Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design

Questionnaire Design

When formulating questions, remember:

  • Clarity:

All notions and concepts should be clearly defined, and ensure that all members of the team and enumerators (if used) have the same understanding of the concept.

  • Simplicity:

Put questions but in a direct form in a vocabulary that is easy to understand. Use local terminology (measures of area, weight).

  • Neutrality:

do not ask ’’leading questions’’ - that is questions that suggest or hint that a particular answer is the correct one.


Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

*

*

CASE STUDY RESEARCH

An Introduction

*

WHY CASE STUDY RESEARCH?

The case study method is amongst the most flexible of research designs. It incorporates a number of data-gathering strategies:

•document analysis, •surveys, •participant or non-participant observation, and •participatory or action research.

*

WHY CASE STUDY RESEARCH? [cont’d]

Case study research can serve a variety of functions:

exploratory (enabling researchers to get a feeling for potentially important variables and to describe phenomena in the appropriate contextual setting),

for testing hypotheses or theories (relating to cause and effect in a quasi-experimental fashion),

and for policy analysis (teasing out prescriptions for action).

*

GENERALIZING FROM CASES

  • One’s ability to generalize from case studies increases with the number of case studies. However, one way to overcome the limitations of a small number of cases is to choose ones that have the greatest variety of characteristics, and that encompass a range of “extremes.”

*

FOCUSES OF STUDY

Case studies are classified according to the focus of study. This can include:

  • Individuals
  • Communities
  • Social groups
  • Organizations and institutions, and
  • Events, roles, relationships, and interactions.

*

PROBLEM OF CASE STUDY RESEARCH

  • Ideally, one should avoid studying an issue solely from the perspective of one stakeholder.

*

TRIANGULATION

There are different kinds of triangulation (ways of “trapping the answers”):

  • Methodological (different types of research methods)
  • Data (different types of data, or replication)
  • Investigator (using more than one), and
  • Theoretical (using different theoretical frameworks).

Triangulation

Key informant interviews

Content analysis of

newspapers

Household survey

Gender/Security/Peace/ Historical accounts

Ecological inventory data

Census data

GIS analysis

Literature

*

*

CONSTRUCT VALIDITY

There are a number of strategies for ensuring construct validity:

  • Using multiple sources of evidence to see if they “converge”
  • Building a sold “chain of evidence”
  • Circulating a case study report to key informants for them to review for accuracy.

*

INTERNAL AND EXTERNAL VALIDITY

Internal validity involves establishing a causal relationship between factors or variables; causality is not the same as correlation

External validity involves establishing the domain to which one’s studies can be generalized. Unless one is studying a large number of cases, the ability to generalize is based on analytical, not statistical, grounds.

*

RELIABILITY

Reliability involves demonstrating that the operations of a study, such as data collection procedures, can be repeated with the same results (i.e. would a different researcher, using the same methods, reach the same conclusions?)

  • To allay concerns, one documents the steps undertaken and keeps proper records – for instance, transcribing interviews and explaining how one “coded” the results. However, no matter how “linear” one tries to be, there is always a certain amount of “doubling back.”

Content Analysis

*

Content Analysis

What Analysis?

Content analysis is a research technique for systematically analysing written communication. It has been used to study books, essays, news articles, speeches, pamphlets and other written material.

Content analysis can help identify propaganda or describe attitudes and psychological states. Despite its name, content analysis is more of a data reduction technique than an analytical one because it breaks down lengthy text material into more manageable units of data.

*

Textbooks, essays, newspapers, novels, magazines, articles, cookbooks, songs, political speeches, advertisements, pictures [even artwork] -- in fact, the contents of virtually any type of communication can be analyzed.

Content Analysis

What Analysis?

A technique that enables researchers to study human behavior in an indirect way:

-- through an analysis of their communications

-- for making theory

-- by analyzing, examining, and selecting data

-- systematically & objectively

SHOULD BE

- connected with what is being discussed

in the messages

- exact wording used in the statement

SHOULD NOT BE

- based on personal opinions

- irrelevant to the messages

*

Content Analysis

Manifest vs. Latent Content Analysis

Manifest content (surface structure):

perceptible, clear, comprehensible message

latent content (deep structure):

implied, un-stated message

*

*

Content Analysis

Unit of Analysis?

Words

Phrases

Sentences

Paragraphs

Blog entries

Video segments

Picture…

Chapters,

Books,

Ideological stance,

Subject topic,

Elements relevant to the context

Sampling

Random Sampling

1. Simple Random Sampling

to draw subjects from an identified population

2. Systematic Sampling

(Interval Random Sampling )

select nth name from the population

Population

Sampling interval = Numbers of persons desired


How to Do Content Analysis

Analysing Text Material

Step 1

State your research question(s). Content analysis can be extremely time-consuming, involving reading and re-reading of a large amount of material.

Knowing what you are trying to find out will help you stay focused in your research and analysis. Suppose, for example, you want to compare newspaper stories on two presidential candidates to see if the coverage was more favourable toward one candidate than the other.

*

Textbooks, essays, newspapers, novels, magazines, articles, cookbooks, songs, political speeches, advertisements, pictures [even artwork] -- in fact, the contents of virtually any type of communication can be analyzed.


How to Do Content Analysis

Analysing Text Material

Step 2

Select your sample of text material. This is what you will read and reduce to a more manageable set of data through a process of reading and categorizing. For our example on news coverage of presidential candidates, let's say the sample is all news stories from five metropolitan newspapers over a one-month period.

Step 3

Read and review the material in your sample. Before beginning any kind of analysis, whether qualitative or quantitative, it is important to examine your data. This is no less true in content analysis.

*

Textbooks, essays, newspapers, novels, magazines, articles, cookbooks, songs, political speeches, advertisements, pictures [even artwork] -- in fact, the contents of virtually any type of communication can be analyzed.


How to Do Content Analysis

Analysing Text Material

Step 4

Define your unit of analysis and categories. The unit of analysis may be specific words, phrases or themes. For this example, the unit of analysis could be specific words describing each candidate that have positive or negative connotations. Keep a dictionary handy for identifying all appropriate words and phrases. Categories, meanwhile, are groups of words, phrases or themes that have similar meanings.

*

Textbooks, essays, newspapers, novels, magazines, articles, cookbooks, songs, political speeches, advertisements, pictures [even artwork] -- in fact, the contents of virtually any type of communication can be analyzed.


How to Do Content Analysis

Analysing Text Material

Step 5

Code the textual material in your sample, marking the keywords or phrases with a pen or highlighter and placing them in the categories you've identified. You can use different colours of pens or highlighters for each category, marking words differently by category - it's up to you.

Remember to make the coding process as easy for yourself as you can. Keep a tally sheet as you code the material. You may have to read everything more than once to identify all keywords and phrases. In doing so, you will have word counts of the frequencies with which certain words and phrases are used in relation to particular candidates.

*

Textbooks, essays, newspapers, novels, magazines, articles, cookbooks, songs, political speeches, advertisements, pictures [even artwork] -- in fact, the contents of virtually any type of communication can be analyzed.


How to Do Content Analysis

Analysing Text Material

Step 6

Interpret and report your findings. Content analysis combines quantitative and qualitative techniques; therefore, writing your findings is in many ways an extension of your analysis.

*

Source: http://classroom.synonym.com/content-analysis-2670.html

*

Qualitative Analysis

Content Analysis

  • Identifying, Coding, Categorizing the primary patterns in the data
  • Interaction styles in online discussion:
  • Complexity of response
  • Question type
  • Levels of argumentation & negotiation
  • Socializing
  • Coding Scheme
  • Creates a scheme which clusters words and phrases into conceptual categories for purposes of counting

*

Use Loret’s Coding Scheme as an Example; also, MySpace Coding.

Example of categorizing information using hand coding

Each response is read and given a code to represent a different concept (category):

Trg = training

T = time

R = resources

P = program

Fdbk = feedback

M= mentor

U = uncertain

Then, the data can be sorted and organized by category to identify patterns and bring meaning to the responses.

*

If you’ve entered your data into a word processing file, you might highlight quotes and type category labels in the margins. It is a good idea to leave a wide margin when you create the file so you have space to type in the margins.

*

Example data set

Or, you might use Excel to organize and categorize your data

*

Identify patterns within and between categories

  • Once you have identified the categories, you might:
  • Sort and assemble all data by theme
  • Sort and assemble data into larger categories
  • Count the number of times certain themes arise to show relative importance (not suitable for statistical analysis)
  • Show relationships among categories

Table __ construction

Table __ construction

Important statistical terms

Population:

a set which includes all

measurements of interest

to the researcher

Sample:

A subset of the population

Why sampling?

Get information about large populations

Less costs

Less field time

More accuracy i.e. Can Do A Better Job of Data Collection

When it’s impossible to study the whole population

Target Population:

The population to be studied/ to which the investigator wants to generalize his results

Sampling Unit:

smallest unit from which sample can be selected

Sampling frame

List of all the sampling units from which sample is drawn

Sampling scheme

Method of selecting sampling units from sampling frame

Types of sampling

Non-probability samples

Probability samples

*

Non probability samples

Convenience samples (ease of access)

sample is selected from elements of a population that are easily accessible

Snowball sampling (friend of friend….etc.)

Purposive sampling (judgemental)

  • You chose who you think should be in the study
  • Quota sample

*

Probability samples

Random sampling

  • Each subject has a known probability of being selected

  • Allows application of statistical sampling theory to results to:
  • Generalise
  • Test hypotheses

*

Conclusions

Probability samples are the best

  • Ensure
  • Representativeness
  • Precision

*

Methods used in probability samples

Simple random sampling

Systematic sampling

Stratified sampling

Multi-stage sampling

Cluster sampling

*

Simple random sampling

Sampling fraction

Ratio between sample size and population size

Systematic sampling

Systematic sampling

Cluster sampling

Cluster: a group of sampling units close to each other i.e. crowding together in the same area or neighborhood

Cluster sampling

Section 4

Section 5

Section 3

Section 2

Section 1

*

MULTISTAGE SAMPLING

*

Complex form of cluster sampling in which two or more levels of units are embedded one in the other.

  • First stage, random number of districts chosen in all

states.

  • Followed by random number of villages.

  • Then third stage units will be houses.

  • All ultimate units (houses, for instance) selected at last step are surveyed.

*

Margin of Error

  • A way of expressing the sampling error in a survey’s results

The larger the margin of error, the less faith one should have that the poll's reported results are close to the "true" figures; that is, the figures for the whole population

Survey Sample Size

  • 2, 000
  • 1, 500
  • 1, 000
  • 900
  • 800
  • 700
  • 600
  • 500
  • 400
  • 300
  • 200

Margin of Error Percent*

2

3

3

3

3

4

4

5

6

7

10

14

*


Margian of Error

 *Assumes  a 95% level of confidence 

*

*

Population and Sample Size

Assuming we wanted to be 95% confident with a margin of error of plus/minus 5%:

Population size Sample size

10 10

50 44

100 80

200 132

500 217

1,000 278

3,000 341

100,000+ 385

Source: Krejcie and Morgan, 1970. Determining Sample Size for Research Activities, Educational and Psychological Measurement 30: 607-610

Dr. G. Johnson, www.researchdemystified.org

Ch 10 Sampling more size and error

*

*

Research Methodology and Design ___The Sample Size

Depends on three factors:

The estimated prevalence of the variable of interest _ e.g. Pregnant women within a theatre of operation;

The desired level of confidence;

The acceptable margin of error

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

*

Research Methodology and Design ____ The Sample Size


Simple random sample, the sample size required is calculated according to this formula:

n = t2 x p(1-p)

e2

Where:

n = required sample size

t = confidence level at 95%

P = estimated prevalence of pregnancy in the study area

e = margin of error at 5% ( standard value of 0.05)

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

*

Research Methodology and Design ____ The Sample Size


Simple random sample, the sample size required is calculated according to this formula:

n = t2 x p(1-p)

e2

p (data) can be taken obtain from published reports: Health centres, Government statistical reports/UNDP etc. Example 20% of national population.

Population of the study area is 124, 050

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

*

Research Methodology and Design ____ The Sample Size


Simple random sample, the sample size required is calculated according to this formula:

Thus n is:

(1) n = 1.962 x 0.2 (1 – 0.2)/0.052

(2) 3.8416 x 0.2 (.8)/.0025

(3) 3.8416 x .16/.0025

(4) .614656/.0025

(5) 245.8624

n= 246

*


Determining Sample Size: How to Ensure You Get the Correct Sample Size (Cont’)

Before you can calculate a sample size, you need to determine a few things about the target population and the sample you need:

Population Size — How many total people fit your demographic? For instance, if you want to know about mothers living in the Accra, your population size would be the total number of mothers living in the Accra. Don’t worry if you are unsure about this number. It is common for the population to be unknown or approximated.

Margin of Error (Confidence Interval) — No sample will be perfect, so you need to decide how much error to allow. The confidence interval determines how much higher or lower than the population mean you are willing to let your sample mean fall. If you’ve ever seen a political poll on the news, you’ve seen a confidence interval. It will look something like this: “68% of voters said yes to Proposition Z, with a margin of error of +/- 5%.”

Source: Scott Smith, (2013). http://www.qualtrics.com/blog/determining-sample-size/


Determining Sample Size: How to Ensure You Get the Correct Sample Size (Cont’)

3. Confidence Level — How confident do you want to be that the actual mean falls within your confidence interval? The most common confidence intervals are 90% confident, 95% confident, and 99% confident.

4. Standard of Deviation — How much variance do you expect in your responses? Since we haven’t actually administered our survey yet, the safe decision is to use .5 – this is the most forgiving number and ensures that your sample will be large enough.

Now that we have these values defined, we can calculate our needed sample size.

Your confidence level corresponds to a Z-score. This is a constant value needed for this equation. Here are the z-scores for the most common confidence levels:

90% – Z Score = 1.645

95% – Z Score = 1.96

99% – Z Score = 2.326

Next, plug in your Z-score, Standard of Deviation, and confidence interval into this equation:*

Necessary Sample Size = (Z-score)² – StdDev*(1-StdDev) / (margin of error)²

Here is how the math works assuming you chose a 95% confidence level, .5% standard deviation, and a margin of error (confidence interval) of +/- 5%.

((1.96)² -0.005 x (1 – 0.005)) / (0.05)²

(3.8416) – 0.005 x(0.995) /0 .0025

308366 x (0.995)/ 0.0025

3.817417/0.0025

1526.9668

1527 respondents are needed

Voila!

  • You’ve just determined your sample size.
  • If you find your sample size is too large to handle, try slightly decreasing your confidence level or increasing your margin of error – this will increase the chance for error in your sampling, but it can greatly decrease the number of responses you need.

Necessary Sample Size = (Z-score)² – StdDev*(1-StdDev) / (margin of error)²

Here is how the math works assuming you chose a 90% confidence level, .5 standard deviation, and a margin of error (confidence interval) of +/- 5%.

=> (1.645)² -.005 * (1 - .005) / (.005)²

=> (2.706025 - .005 (.995) / 2.5 E – 5

=> (2.706025 - .004975) / 2.5 E – 5

=> (2.70105)/ 2.5 X 10 – 5

NB: 10 – 5 means move five decimal places to the left = 0.000025

=> 2.70105/0.000025

Ans: 108042 respondents are needed

Voila!

  • You’ve just determined your sample size.

P values _ re-cap

  • P values = the probability that the observed result was obtained by chance
  • i.e. when the null hypothesis is true
  • α level is set a priori (Usually 0.05)

  • If p < α level then we reject the null hypothesis and accept the experimental hypothesis
  • 95% certain that our experimental effect is genuine

  • If however, p > α level then we reject the experimental hypothesis and accept the null hypothesis

*

P = Probability

this value tells us the probability that the observed result was obtained by chance

That there is no difference between the two groups

Each test result (e.g. t value) is associated with a particular p value

α level is set a priori

This is basically an acceptance level

Usually this is set to 0.05

But as I understand, α levels are usually much lower than this in fMRI

If p < α level then we reject the null hypothesis and accept the experimental hypothesis

- concluding that we are 95% certain that our experimental effect is genuine

If however, p > α level then we reject the experimental hypothesis and accept the null hypothesis

- that there was no sig diff in brain activation levels between the two conditions

11.*

Concepts of Hypothesis Testing (1)…

The two hypotheses are called the null hypothesis and the other the alternative or research hypothesis. The usual notation is:

  • H0: — the ‘null’ hypothesis
  • H1: — the ‘alternative’ or ‘research’ hypothesis

pronounced

H “nought”

Type I Error

H0 ------true

But we reject H0

Example: Innocent but found guilty.

  • Type II Error

  • H0 ------false
  • But we fail to reject H0

Example: Guilty but food innocent

Two types of errors

Subject matter of lecture: Science and Research __ Quantitative research

We will briefly address the following questions:-  

  • What are quantitative methods? 
  • What are the ingredients of quantitative methods?
  • How do you go about research design?



Quantitative Research



*

Subject matter of lecture: Science and Research __ Quantitative research

Quantitative research methods were originally developed in the natural sciences to study natural phenomena.

However examples of quantitative methods now well accepted in the social sciences and education include:

  • surveys;
  • laboratory experiments;
  • formal methods such as econometrics:
  • numerical methods such as mathematical modelling.



Quantitative Research



*

Subject matter of lecture: Science and Research __ Quantitative research

Quantification can be useful, because it can

■ provide a broad familiarity with cases;

■ examine patterns across many cases;

show that a problem is numerically significant;

often be used as the starting point for a qualitative study;

■ provide readily available and unambiguous information.



Quantitative Methods



*

Subject matter of lecture: Science and Research __ Quantitative research

Quantitative research has positivist features when it:

■ tries to link variables (features which vary from person to person);

■ tries to test theories or hypotheses;

■ tries to predict;

■ tries to isolate and define categories before research starts and then to determine the relationships between them.



Quantitative Methods



*

Subject matter of lecture: Science and Research __ Quantitative research

Quantitative research:

Procedures are standard, and replication is assumed

Analysis proceeds by using statistics, tables, or charts and discussing how what they show relates to hypotheses



Quantitative Methods



*

Subject matter of lecture: Statistical Concepts

Variables __ dependent or independent

We may collect data concerning many variables, perhaps through a questionnaire, or choose to measure just two or several variables by observation or testing.

The variables we are interested in may be dependent or independent.



Quantitative Methods



*

Subject matter of lecture: Statistical Concepts

Data

Using the data that you have collected then you can:

Describe variables in terms of distribution:

Frequency, central tendency and measures and form of dispersion.

Descriptive statistics include averages, frequencies, cumulative distributions, percentages, variance and standard deviations, associations and correlations.

Variables can be displayed graphically by tables, bar or pie charts for instance.



Quantitative Methods



*

Subject matter of lecture: Statistical Concepts

Data

Using the data that you have collected then you can:

Describe variables in terms of distribution:

In fact univariate (one variable) analysis can only be descriptive.

But descriptive statistics can be used to describe a significant relationship between two variables (bivariate data) or more variables (multivariate).



Quantitative Methods



*

Pause __

Sampling __Key themes

A famous sampling mistake

Quantitative assumptions in sampling

Qualitative assumptions in sampling

Types of sampling

Ethnographic sampling

Interview sampling

Content analysis sampling

How many?

*

A famous sampling mistake

*

A famous sampling mistake

That’s Truman

They only asked rich, white people with telephones who’d they vote for. Sadly, they published their mistake

*

Even with proper sampling…beware!

“…predicting behavior on the basis of knowledge of attitude is a very hazardous venture.” Meaning, predicting social behavior is often misguided. Keep that in mind!

*

What exactly IS a “sample”?

*

What exactly IS a “sample”?

A subset of the population, selected by either “probability” or “non-probability” methods. If you have a “probability sample” you simply know the likelihood of any member of the population being included (not necessarily that it is “random.”

*

I want to know what causes something else.

What do quant researchers worry about?

I really spend a lot of time wondering how to measure things.

I wonder how small patterns generalize to big patterns.

I want to make sure others can repeat my findings.

*

Assumptions of quantitative sampling

We want to generalize to the population.

Random events are predictable.

Therefore…

We can compare random events to our results.

Probability sampling is the best approach.

*

I want to see the world through the eyes of my respondents.

What do qual researchers worry about?

I want to describe the context in a lot of detail.

I want to show how social change occurs. I’m interested in how things come to be.

I really want my research approach to be flexible and able to change.

*

Assumptions of qualitative sampling

Social actors are not predictable like objects.

Randomized events are irrelevant to social life.

Probability sampling is expensive and inefficient.

Therefore…

Non-probability sampling is the best approach.

*

Types of samples

*

Simple Random Sample

Get a list or “sampling frame”

This is the hard part! It must not systematically exclude anyone.

Remember the famous sampling mistake?

Generate random numbers

Select one person per random number

Systematic Random Sample

Select a random number, which will be known as k

Get a list of people, or observe a flow of people (e.g., pedestrians on a corner)

Select every kth person

Careful that there is no systematic rhythm to the flow or list of people.

If every 4th person on the list is, say, “rich” or “senior” or some other consistent pattern, avoid this method

Stratified Random Sample

Separate your population into groups or “strata”

Do either a simple random sample or systematic random sample from there

Note you must know easily what the “strata” are before attempting this

If your sampling frame is sorted by, say, school district, then you’re able to use this method

Multi-stage Cluster Sample

Get a list of “clusters,” e.g., branches of a company

Randomly sample clusters from that list

Have a list of, say, 10 branches

Randomly sample people within those branches

This method is complex and expensive!

 

The Convenience Sample

Find some people that are easy to find

*

The Snowball Sample

Find a few people that are relevant to your topic.

Ask them to refer you to more of them.

*

The Quota Sample

Determine what the population looks like in terms of specific qualities.

Create “quotas” based on those qualities.

Select people for each quota.

*

The Theoretical Sample

*

What about generalizing?

“Our findings have a margin of error of + or - 5%, 19 times out of 20.”

“The average man is 35% more likely to choose this option over the average woman.”

*

Proviso in non-probability sampling: no generalizing

“Our findings have a margin of error of + or - 4%, 19 times out of 20.”

“The average man is 35% more likely to choose this option over the average woman.”

*

*

Samples: How Many?

When working with non-random samples, size is not that important because researchers know that they can not generalize to the larger population

  • Face validity is sufficient

Dr. G. Johnson, www.researchdemystified.org

Ch 10 Sampling more size and error

*

*

Sample: How Many?

When working with random sample data, size matters

Researchers want a big enough sample so they can be reasonably confident that the results are a fairly accurate reflection of the population

Statisticians have figured this out.

*

Random Sample Size

  • Sample size is a function of three things:

Size of the population of interest

Decision about how important is it to be accurate?

  • Confidence level

Decision about how important is to be precise?

  • Sampling error (also called margin of error) or confidence interval
  • In general, accuracy and precision is improved by increasing the sample size

*

Random Samples is Based on Probabilities

If we selected 1,000 random samples, the results for average height would theoretically form a bell-shaped curve (normal curve)

This means that 95% of the samples would show an average height that was plus or minus 2 standard deviations.

This statistical magic allows statisticians to estimate the probability of getting results from a random sample that are outside of that 95%

Dr. G. Johnson, www.researchdemystified.org

Ch 10 Sampling more size and error

*

Central Limit Theorem:

The sample avenge is approximately normally distributed.

68% of the values are within 1 standard deviation of the mean.

95% of the values are within 2 standard deviation of the mean.


Standard Deviation and Variance

Standard Deviation __ Deviation just means how far from the normal

The Standard Deviation is a measure of how spread out numbers are.

Its symbol is σ (the greek letter sigma)

The formula is easy: it is the square root of the Variance. So now you ask, "What is the Variance?“

Variance

The Variance is defined as:

  • The average of the squared differences from the Mean.


Standard Deviation and Variance

Variance

To calculate the variance follow these steps:

  • Work out the Mean (the simple average of the numbers)
  • Then for each number: subtract the Mean and square the result (the squared difference).
  • Then work out the average of those squared differences. (Why Square?

Example


Standard Deviation and Variance

Variance

Example

  • You and your friends have just measured the heights of your dogs (in millimetres):

The heights (at the shoulders) are:

600mm, 470mm, 170mm, 430mm and 300mm.


Standard Deviation and Variance

Variance

Example

Your first step is to find the Mean:

Answer:


Mean =   600 + 470 + 170 + 430 + 300   =   1970   = 394
5 5


Standard Deviation and Variance

Variance

Example

Answer:

So the mean (average) height is 394 mm. Let's plot this on the chart:



Standard Deviation and Variance

Variance

Example

Answer:

Now, we calculate each dogs difference from the Mean:



Standard Deviation and Variance

Variance

Example

Answer:

To calculate the Variance, take each difference, square it, and then average the result:

So, the Variance is 21,704.

  • And the Standard Deviation is just the square root of Variance, so:

Standard Deviation: σ = √21,704 = 147.32... = 147 (to the nearest mm)



Standard Deviation and Variance

Variance

Example

Answer:

And the good thing about the Standard Deviation is that it is useful. Now we can show which heights are within one Standard Deviation (147mm) of the Mean:

So, using the Standard Deviation we have a "standard" way of knowing what is normal, and what is extra large or extra small.



Standard Deviation and Variance

Variance

Example

Answer:

But ... there is a small change with Sample Data

Our example was for a Population (the 5 dogs were the only dogs we were interested in).

  • But if the data is a Sample (a selection taken from a bigger Population), then the calculation changes!
  • When you have "N" data values that are:



Standard Deviation and Variance

Variance

Example

Answer:

When you have "N" data values that are:

  • The Population: divide by N when calculating Variance (like we did)
  • A Sample: divide by N-1 when calculating Variance
  • All other calculations stay the same, including how we calculated the mean.



Standard Deviation and Variance

Variance

Example

Answer:

Example: if our 5 dogs were just a sample of a bigger population of dogs, we would divide by 4 instead of 5 like this:

Sample Variance = 108,520 / 4 = 27,130

Sample Standard Deviation = √27,130 = 164 (to the nearest mm)

Think of it as a "correction" when your data is only a sample.



Standard Deviation and Variance

Formulas

Here are the two formulas, explained at Standard Deviation Formulas if you want to know more:

Looks complicated, but the important change is to divide by N-1 (instead of N) when calculating a Sample Variance.


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Bell-Shaped Curve
(Normal Curve)

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Normal Curve Explained

  • This is called a normal distribution.

  • If we were to line up 1,000 people on the soccer field according to their height, they would look like a bell.

  • At the center, is the average or mean. The highest number of people would be of average height.

  • To the right side, would be the number of people who were taller than the average height, and to the left would be the people shorter than the average height.

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Normal Curve Explained

  • The properties of the normal distribution are that 68% are within a set distance from the mean (one standard deviation) and 95 percent are within two standard deviations from the mean.

  • For our purposes here, we just need to takeaway the point that statisticians have figured out how to estimate how 95% of a given population is likely to be distributed.
  • They can estimate the height of 95% of the people standing out on the soccer field.

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Statistical Magic

  • This ability to estimate distributions allows statisticians to provide researchers with a level of confidence about results from a random sample.

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What Does Confidence Mean?

How confident do you want to be that the sample result is reasonably accurate?

The standard is a 95% confidence level:

  • This means that 19 out of 20 random samples would have found similar results that we found from this random sample
  • Or that we are 95% certain that the sample results are a reasonably accurate estimate of the population

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What Does Precision Mean?

  • Sampling Error in survey results is one way to estimate precision:
  • The social and/or science standard is plus and minus 5%.

  • We obtained these survey results:
  • 45% oppose building a dam and 50% favor building a dam.
  • The margin of error is +/- 5%.

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Margin of Error

  • A way of expressing the sampling error in a survey’s results

The larger the margin of error, the less faith one should have that the poll's reported results are close to the "true" figures; that is, the figures for the whole population

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Margin of Error

  • If the margin of error overlaps, it means the results are too close to call for the population as whole

  • Think of election polls: if the survey results say 49% favor X and 46% favor Y, with a +/-5% margin of error, the race is too close to call. It is just as probably that 46% favor X and 49% favor Y

Survey Sample Size

  • 2, 000
  • 1, 500
  • 1, 000
  • 900
  • 800
  • 700
  • 600
  • 500
  • 400
  • 300
  • 200
  • 100
  • 50

Margin of Error Percent*

2

3

3

3

3

4

4

5

6

7

10

14

*


Margian of Error

 *Assumes  a 95% level of confidence 

*

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Population and Sample Size

Assuming we wanted to be 95% confident with a margin of error of plus/minus 5%:

Population size Sample size

10 10

50 44

100 80

200 132

500 217

1,000 278

3,000 341

100,000+ 385

Source: Krejcie and Morgan, 1970. Determining Sample Size for Research Activities, Educational and Psychological Measurement 30: 607-610

Dr. G. Johnson, www.researchdemystified.org

Ch 10 Sampling more size and error

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*

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Another View of Sample Error

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http://en.wikipedia.org/wiki/Margin_of_error

Dr. G. Johnson, www.researchdemystified.org

Ch 10 Sampling more size and error

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My Best Advice

Use the entire population whenever possible

If it is necessary to use a random sample, sample large

  • The calculated sample sizes should be seen as minimums

There is nothing more frustrating than getting to the end of a study to discover that the sample size was too small to give statistically valid results

Ethnographers sample…

People

Places

Contexts

Times

Events

*

Interviewers sample…

People

Places

Times

*

Content analysts sample…

Media

Dates

*

 two general guidelines: the number of participants is sufficient when…

…the extent to which the selected participants represent the range of potential participants in the setting

…the point at which the data gathered begins to be redundant (“data saturation”)

The threats to validity in qualitative studies...

  • observer bias…
  • observer effects…

…invalid information resulting from the perspective the researcher brings to the study and imposes upon it

…the impact of the observer’s participation on the setting or the participants being studied

 extend the time for observing the setting

 include more participants to make the study more representative

 focus upon building participant trust in order to access more detailed and honest data

d. strategies to enhance validity and to reduce bias...

 journalize one’s own reflections, concerns, and uncertainties during the study and refer to them when examining the data

 carefully examine unusual or contradictory results for explanations (“outliers”)

 utilize a variety of data sources to confirm one another to corroborate participant information (“triangulation”)

1. The qualitative research proposal...

 identifies setting or context of study

 specifies the kinds of data to be collected

 defines area of study

 describes methods to be used

 provides the researcher’s rationale for undertaking the study

 identifies the study’s potential contribution(s)

2. Intensive participation in a field setting...

 approach to participation: overt or covert

 participation: as a participant (“participant observer”) or non-participant

 requires experiencing the situation from the perspective of both an observer and a participant

3. Collecting and analyzing data...

 multiple data sources are normative

 primary tools include observations and interviews but can also include personal and official documents, photographs, recordings, drawings, emails, and informal conversations

regarding field notes…

…put aside assumptions, experience context first

…see phenomena through participants’ perspective

…write up notes immediately following an observation

…detail is critical: include date, site, time, and topic on every set of field notes; leave wide margins for writing impressions; use only one side of a page of paper; draw diagram of site (if necessary)

…list key words first, then outline one’s observations

…keep the descriptive and reflective sections separate

…number the lines or paragraphs for easy access

regarding interviews…

…the purpose is to explore and to probe the interviewee’s responses in order to gather in-depth data

…the interviewer inquires into the interviewees’ attitudes, interests, feelings, concerns, and values as these relate to the context being studied

…be alert for openings in responses to probe more deeply, starting with mundane questions and gradually easing into more sensitive and more complex questions

…interview data collection techniques include taking notes during the interview, writing notes after the interview, or tape recording and transcribing the interview (the transcript is a “verbatim”)

Interview do’s and don’ts...

 Do follow up on what is not clear and probe more deeply into what is revealed

 Do listen more and talk less

 Don’t use leading questions; do use open-ended questions (“probes”)

 Don’t interrupt; do wait

 Do ask for concrete details

 Do keep interviewee(s) focused

 Do tolerate silence and space between interviewee’s responses; do allow the interviewee time to think

 Don’t be judgmental about or react to an interviewee’s opinions, views, or beliefs

. classifying the data, including categorization, coding, and grouping into thematic units

. interpreting and synthesizing the organized data into general conclusions or understandings

Analyzing field data…

data pieces

data categories

data patterns

  • Criteria suitable for qualitative data analysis...

a. credibility or plausibility

b. transferability

c. including a methods section

credibility or plausibility

…to demonstrate that the study was conducted in such a manner as to ensure that the subject was accurately identified and described

transferability

…to demonstrate that the results of the study are generalizable to others in the original research context or to contexts beyond the original study

including a methods section

…to provide an in-depth description of the processes and methods used in the study

5. Writing the research report...

 provide a setting where the data were collected

 identify characters who provide information

 describe the social action in which the characters are engaged

 offers an interpretation of what the social action means to the characters

Mini-Quiz…

1. True and false…

…Qualitative research methods are rooted in the disciplines of sociology, anthropology, and history rather than in mathematics.

2. True and false…

…The central focus of qualitative research is to provide understanding of a social setting or activity from the perspective of the research participants

3. True and false…

…Empathic neutrality requires a researcher to include one’s personal experience and empathic insight as part of the relevant data

4. True and false…

…One of the first issues in qualitative research is to gain entry to a site

5. True and false…

…One indicator that an adequate number of participants has been selected is the extent to which the selected participants represent the range of potential participants in the setting

6. True and false…

…Purposive sampling strategies are especially useful in qualitative research

7. True and false…

…A qualitative researcher should be wary of potential participants who are extremely eager to be included in the study

8. True and false…

…A covert participant observer participates as well as collects data during an observation session

9. True and false…

…Each observation session has its unique focus and interactions but is guided by a protocol or list of issues that frame the observation

10. True and false…

…An interviewer will almost always meet face-to-face with an interviewee while some observers will not.

11. Fill in the blank…

…Immersion in the details and specifics of the data to discover important categories, dimensions, and interrelationships; begins by exploring genuinely open questions rather than testing theoretically derived hypotheses _____________

12. Fill in the blank…

…The entire phenomenon is understood as a complex system more than the sum of its parts; the focus is upon complex interdependencies not meaningfully reduced to a few discrete variables and linear, cause-effect relationships________________

13. Fill in the blank…

…A detailed, thick description; inquiry in depth; direct quotations capturing people’s personal perspectives and experiences_______________

Mini quiz __ answers

True

True

True

True

True

True

True

True

True

True

Inductive analysis

Holistic perspective

Qualitative data

What are Some Qualitative Methods?

  • Interviews
  • Participant-Observation
  • Focus Groups
  • (Certain Forms of) Text and Image Analysis
  • Diary Studies

Any others?

But recall Creswell piece…’qualitative approaches’ are not defined simply by methods.

Also ‘knowledge claims’ and ‘strategies of inquiry’

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Qualitative Research Stereotypes

  • is not generalizable / is “anecdotal”

  • The sample is too small to say anything / is not a random sample / not representative

  • Very interesting, but can you show me some data that supports your claims?
  • the researcher’s presence in the setting biases the results
  • lacks rigor, procedure is unsystematic

Population generalizability is but one way of talking about and thinking about generalizability. We can also think about generalizing to theory. Abstractions can be drawn from small samples, from the atypical to build, refine or critique theory.

Some research is about identifying what is typical, some research is not.

Interview transcripts, ethnographic field notes are data. Data does not equal numbers.

Lacks rigor? How are we defining rigor?

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Qualitative Research – Distinctive Points of Emphasis, Priorities

  • Naturalistic Observation – how things unfold out in the real world (uncontrived)

  • Interested in Subjective Meanings (of Research Subjects) – ascertaining and analyzing the actor’s point of view (opinion, attitude, belief, value)
  • Inductive Analysis – on the side of theory discovery rather than theory testing

We should evaluate qualitative research by criteria that match with these points of emphasis these priorities.

Not hold it to criteria from other research traditions.

So let’s talk about these priorities, what they demand, and what trade-offs are involved.

Trade offs of ‘naturalistic observation’:  

  • lack of control over variables (as you would get in experimentation), (2) to get that close you have to be there which means you as a researcher can potentially disrupt, distort (sometimes that is actually very illuminating – people will explain the taken for granted to a stranger an outsider).

Trade offs of dealing with ‘subjective meanings of research subjects’

unavoidably face the limits of human communication and of ever knowing what is in someone’s mind.…also the flexibility of techniques required to invite that kind of self-expression creates some difficulty in doing comparisons – apples to oranges.

Trade offs of doing ‘inductive analysis’

not oriented to establishing causality, not well suited to generating precise measures of magnitude or distribution of a phenomenon

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Qualitative Research – value in product /technology design specifically

[see Blomberg et al. 2003 for more]

Naturalistic Observation More sound basis for feature prioritization exercises … beyond the focus group or big n marketing research surveys (de-contextualized, self-report)
Subjective Experience (of research subjects) Getting a handle on ever more diverse user populations whose experiences and values are very different from our own
Inductive Analysis Design innovation work…discovery process

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PROCESS

The Question of Rigor in Quantitative vs. Qualitative Approaches

Problem

Method

Data Collection

Support or Reject

Hypotheses

Process: How Quantitative Research Really Works…

If we strip it all down, this is one of the ways in terms of an orderly sequence - we are taught (and teach others) to do user research in the traditional, positivist framework.

The reality should not prevent us from having a logic to our work, especially when we formulate our HYPOTHESES.

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Process in Qualitative Research

An Iterative Approach
(Inductive Analysis)

1) research topic/questions

2) sampling, site selection

3) data gathering

4) analysis

5) write-up

4) more analysis

Field work

Quantitative is less sequential, linear, and orderly than one might presume.

Likewise Qualitative Research has an underlying structure guiding such work…not sequential, but iterative

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The epistemology of qualitative research (Criteria for Evaluation)

Quantitative Tradition Qualitative Tradition
Reliability – reproducing the findings through the same procedures, same findings from multiple observers Accuracy – based on close observation not remote indicators
Validity – whether and how well the researchers measured the phenomenon they claimed to be dealing with Precision – captures a fine-grained account of the phenomenon including its dimensions and variation
Breadth – knowledge of a broad range of matters that touch on the topic

On the qualitative side:

  • Embraces complexity, contradiction – goes along with precision

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Historical Research

…studies available data to study, understand, and interpret past events

Source: https://www.google.com.gh/?gws_rd=cr,ssl&ei=Kt8VVKufLcfiaIb2gegI#q=Historical+Studies+ppt

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Historical Research

What is Historical Research?

The systematic collection and evaluation of data to describe, explain, and understand actions or events that occurred sometime in the past.

  • There is no manipulation or control of variables as in experimental research.

  • An attempt is made to reconstruct what happened during a certain period of time as completely and accurately as possible.

The Purposes and/or Value of Historical Research

It throws light on present and future trends.

It enables understanding of and solutions to contemporary problems to be sought in the past.

It can illuminate the effects of key interactions within a culture or sub-culture.

It allows for the revaluation of data in relation to selected hypotheses, theories and generalizations that are presently held about the past and the present.

Steps in a Historical-Comparative Research Project

1. Conceptualization of an idea, topic, or research question

2. Locate evidence and do background literature review

3. Evaluate evidence

4. Organize evidence

5. Synthesize evidence and develop general explanatory model

6. Develop a narrative exposition of the findings

Categories of Sources

Documents are written or printed materials that have been produced in some form or another.

Numerical records can be considered as a separate type of source in and of themselves or as a subcategory of documents.

Oral Statements are stories or other forms of oral expression that leave a record for future generations.

Relics are any objects whose physical or visual characteristics can provide some information about the past.

Evaluating Sources

External Criticism:

  • Appraises the authenticity and authorship of the data source

Internal Criticism:

  • Appraises the meaning and intent of the data source

Types Of Historical Research

A. Historical Events Research

  • examines particular events or processes that occurred over short spans of time
  • Methodological problems
  • Meanings may have changed
  • Information may not be complete

Types (cont.)

B. Historical Process Research

  • focus on how and why a series of events unfolded over some period of time

  • Methodological problems:
  • May place too much emphasis on the actions and decisions of particular actors
  • Not always clear which example represents general pattern
  • definitions may change over time
  • relies on long-term records and archives

Types (cont.)

C. Cross-Sectional Comparative Research

  • comparing two or more social settings or groups (usually countries) at one particular point in time

  • Methodological problems:
  • comparability of measures across countries

Types (cont.)

D. Comparative Historical Research

  • combines historical process research

and cross-sectional comparative research

  • To understand causal processes at work within particular groups and to identify general historical patterns across groups

  • Methodological problems:
  • history has not been recorded accurately or reliably
  • difficult to know how to deal with exceptions
  • difficult to conclude that one factor (and not others) is what causes some outcome
  • groups being compared may not be independent (Galton’s Problem)

Primary vs. Secondary Sources

A primary source is one prepared by an individual who was a participant in or a direct witness to the event being described.

A secondary source is a document prepared by an individual who was not a direct witness to an event, but who obtained a description of the event from someone else.

Running Records

  • Statistics, gov’t data

Data Analysis
in Historical Research

Historical researchers use the following methods to make sense out of large amounts of data:

Theoretical model leading to a content analysis

Use of patterns or themes

Coding system

Quantitative data to validate interpretations

Advantages and Disadvantages
of Historical Research

Advantages

  • Permits investigation of topics and questions that can be studied in no other fashion

  • Can make use of more categories of evidence than most other methods (with the exception of case studies and ethnographic studies)

Disadvantages

  • Cannot control for threats to internal validity

  • Limitations are imposed due to the content analysis

  • Researchers cannot ensure representation of the sample

Advantages and Disadvantages
of Historical Research

Advantages

  • The historical method is unobtrusive

  • The historical method is well suited for trend analysis

  • There is no possibility of researcher-subject interaction.

Disadvantages

  • Bias in interpreting historical sources.

  • Interpreting sources is very time consuming.

  • Sources of historical materials may be problematic

  • Lack of control over external variables

Historical Research is Not as Easy as You Think

Understanding Complex Policy and Management Issues in their Real World Context:

Case Study Approaches to Research

Why Case Study Research?

  • It is one of several ways of doing research.

“In general, case studies are preferred when (a) “how” and “why” questions are being posed, (b) the investigator has little control over events, and the focus is on contemporary phenomenon within a real-life context” (Yin, 2009. p. 2).

Case Study Definition

“Case study is a strategy for doing research which involves an empirical investigation of a particular contemporary phenomenon with its real life context using multiple sources of evidence” (Yin, 1981).

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Definition of a Case Study is Two-Fold

  • A case study is an inquiry that
  • investigates a contemporary phenomenon in depth and within a real-life context, especially when
  • the boundaries between phenomenon and context are not really clear
  • Relies on multiple sources of evidence, with data needing to converge in a triangulation fashion and as another result

Benefits from the prior development of theoretical propositions to guide data collection and analysis (Yin, 2009. 18).

Designing the Case Study

What is the phenomenon being studied? Define the case – What are the boundaries?

What are the research questions?

Who are the key players?

What are the key social, economic, ecological, political, security, peace, gender factors? (Describe the context).

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Designing the Case Study

What data will be required?

How will data be collected?

How will data be analyzed?

What will be the utility of study results? For whom?

How will study results be disseminated?

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Triangulation

  • It is generally accepted in action research “that researchers should not rely on any single source of data, interview, observation, or instrument” (Mills, 2003, p. 52).

  • “In research terms, this desire to use multiple sources of data is referred to as triangulation” ( Mills, 2003, p. 52).

Triangulation

Key informant interviews

Content analysis of

newspapers

Household survey

Defence/Gender/Security/Peace/Historical accounts/International Relations

Ecological inventory data

Census data

GIS analysis

Literature

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Types of Triangulation

  • Yin (2009) describes four types of triangulation

  • Data source (multiple data sources)
  • Investigator(multiple investigators)
  • Theories (Conceptual frameworks)
  • Methodological (multiple data collection methods)

Data Sources

Yin (2009) Recommends six sources of data for case studies

  • Documentation
  • Archival Records
  • Interviews (or surveys)
  • Direct observation/Participant observation
  • Physical artifacts

What is the difference between direct observation and participant observation

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Data Collection methods

Creswell and Clark (2007) recommend mixed method data collection, i.e., using both quantitative and qualitative data collection methods to strengthen the validity of the conclusions that you reach.

What is quantitative data? What is qualitative data?

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Theoretical Propositions

Literature Review (LR)

Analysis of your environment (AN)

Concept Map (CM) is a function of the literature review and your analysis of your environment, i.e., CM = f (LR*AN)

Your concept map is the theoretical proposition for your case study

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Validity

Validity – generally there are four types:

  • Construct validity – identifying correct operational measures for the concepts being studied _ (determines whether the program measured the intended attribute)
  • Internal validity – does your concept map work the way you predicted_ (Could there be an alternative cause, or causes, that explain my observations and results?)
  • External validity – does your study add to the theoretical understanding of the concepts
  • Reliability – demonstrating that the operations of the study can be repeated with the same results (Yin, 2009).

Judging Case Study Design Quality
(after Yin 2003).

Tests Case Study Tactic
Construct validity Multiple data sources Chain of evidence Informant review
Internal validity Pattern matching Rival explanations Logic models
External validity Theory – base (single cases) Replication logic (multiple cases)
Reliability Case study protocol Database

Generalizing from Case Studies

Statistical generalization: describing a population based upon a sample.

  • Theoretical (analytical) generalization: describing a phenomenon based upon a case.

Data Analysis

Data management

  • To record or not to record
  • Transcribing interviews
  • Coding
  • Pattern recognition
  • Writing as analysis

Coding Text

“Selective Coding”

Families

“Axial Coding”_ disaggregation of core themes

“Open Coding” _initial phase of the coding process

Increasing Abstraction

Criticisms of Case Studies

Lack of rigor of case study research.

Provide little basis for scientific generalization.

Case Studies take too long.

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  • (they are generalizable to theoretical propositions and not to populations or universes.)
  • (do not necessarily have to be long, as one could do a case study without ever having to leave the library or telephone.)

Content Analysis

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Content Analysis

What Analysis?

Content analysis is a research technique for systematically analysing written communication. It has been used to study books, essays, news articles, speeches, pamphlets and other written material.

Content analysis has been defined as a systematic, replicable technique for compressing many words of text into fewer content categories based on explicit rules of coding (Berelson, 1952; GAO, 1996; Krippendorff, 1980; and Weber, 1990).

Content analysis enables researchers to sift through large volumes of data with relative ease in a systematic fashion (GAO, 1996).

It can be a useful technique for allowing us to discover and describe the focus of individual, group, institutional, or social attention (Weber, 1990).

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Textbooks, essays, newspapers, novels, magazines, articles, cookbooks, songs, political speeches, advertisements, pictures [even artwork] -- in fact, the contents of virtually any type of communication can be analyzed.

Content Analysis

What Analysis?

A technique that enables researchers to study human behavior in an indirect way:

-- through an analysis of their communications

-- for making theory

-- by analyzing, examining, and selecting data

-- systematically & objectively

SHOULD BE

- connected with what is being discussed

in the messages

- exact wording used in the statement

SHOULD NOT BE

- based on personal opinions

- irrelevant to the messages

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Content Analysis

Manifest vs. Latent Content Analysis

Manifest content (surface structure):

perceptible, clear, comprehensible message

latent content (deep structure):

implied, un-stated message

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*

Content Analysis

Unit of Analysis?

Words

Phrases

Sentences

Paragraphs

Blog entries

Video segments

Picture…

Chapters,

Books,

Ideological stance,

Subject topic,

Elements relevant to the context

Sampling

Random Sampling

1. Simple Random Sampling

to draw subjects from an identified population

2. Systematic Sampling

(Interval Random Sampling )

select nth name from the population

Population

Sampling interval = Numbers of persons desired

3. Stratified Sampling

- divide population into stratum

- ensure : dissimilarity between stratum ↑

similarity inside of each strata ↑

∴ produce a representative sample

II. Non-random Sampling

Purposive Sampling

researcher select subjects according to

his/her research purpose and understanding of the population

- researcher: with sufficient knowledge or expertise

- subjects: represent the population


How to Do Content Analysis

Analysing Text Material

Step 1

State your research question(s). Content analysis can be extremely time-consuming, involving reading and re-reading of a large amount of material.

Knowing what you are trying to find out will help you stay focused in your research and analysis. Suppose, for example, you want to compare newspaper stories on two presidential candidates to see if the coverage was more favourable toward one candidate than the other.

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Textbooks, essays, newspapers, novels, magazines, articles, cookbooks, songs, political speeches, advertisements, pictures [even artwork] -- in fact, the contents of virtually any type of communication can be analyzed.


How to Do Content Analysis

Analysing Text Material

Step 2

Select your sample of text material. This is what you will read and reduce to a more manageable set of data through a process of reading and categorizing. For our example on news coverage of presidential candidates, let's say the sample is all news stories from five metropolitan newspapers over a one-month period.

Step 3

Read and review the material in your sample. Before beginning any kind of analysis, whether qualitative or quantitative, it is important to examine your data. This is no less true in content analysis.

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Textbooks, essays, newspapers, novels, magazines, articles, cookbooks, songs, political speeches, advertisements, pictures [even artwork] -- in fact, the contents of virtually any type of communication can be analyzed.


How to Do Content Analysis

Analysing Text Material

Step 4

Define your unit of analysis and categories _The unit of analysis may be specific words, phrases or themes. For this example, the unit of analysis could be specific words describing each candidate that have positive or negative connotations. Keep a dictionary handy for identifying all appropriate words and phrases.

Categories, meanwhile, are groups of words, phrases or themes that have similar meanings.

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Textbooks, essays, newspapers, novels, magazines, articles, cookbooks, songs, political speeches, advertisements, pictures [even artwork] -- in fact, the contents of virtually any type of communication can be analyzed.


How to Do Content Analysis

Analysing Text Material

Step 5

Code the textual material in your sample __marking the keywords or phrases with a pen or highlighter and placing them in the categories you've identified. You can use different colours of pens or highlighters for each category, marking words differently by category - it's up to you.

Remember to make the coding process as easy for yourself as you can. Keep a tally sheet as you code the material. You may have to read everything more than once to identify all keywords and phrases. In doing so, you will have word counts of the frequencies with which certain words and phrases are used in relation to particular candidates.

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Textbooks, essays, newspapers, novels, magazines, articles, cookbooks, songs, political speeches, advertisements, pictures [even artwork] -- in fact, the contents of virtually any type of communication can be analyzed.


How to Do Content Analysis

Analysing Text Material

Step 6

Interpret and report your findings.

Content analysis combines quantitative and qualitative techniques; therefore, writing your findings is in many ways an extension of your analysis.

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Source: http://classroom.synonym.com/content-analysis-2670.html

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Quantitative Content Analysis

Identify categories

Count frequencies of word occurrence & run statistical analysis

*

Not a mechanical count of words; validity-established coding scale, automated on a computer.

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Qualitative Analysis

Content Analysis

  • Identifying, Coding, Categorizing the primary patterns in the data
  • Interaction styles in online discussion:
  • Complexity of response
  • Question type
  • Levels of argumentation & negotiation
  • Socializing
  • Coding Scheme

Creates a scheme which clusters words and phrases into conceptual categories for purposes of counting

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Use Loret’s Coding Scheme as an Example; also, MySpace Coding.

Example of categorizing information using hand coding

Each response is read and given a code to represent a different concept (category):

Trg = training

T = time

R = resources

P = program

Fdbk = feedback

M= mentor

U = uncertain

Then, the data can be sorted and organized by category to identify patterns and bring meaning to the responses.

*

If you’ve entered your data into a word processing file, you might highlight quotes and type category labels in the margins. It is a good idea to leave a wide margin when you create the file so you have space to type in the margins.

*

Example data set

Or, you might use Excel to organize and categorize your data

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  • Once you have identified the categories, you might:
  • Sort and assemble all data by theme
  • Sort and assemble data into larger categories

Count the number of times certain themes arise to show relative importance (suitable for statistical analysis)

  • Show relationships among categories



Identify patterns within and between categories

Table __ construction

Table __ construction

Interpretation and Analysis __qual.

The narrative responses may be brief or very long and detailed.

Your job is to make sense of these data and to make them understandable for others.

Now, stand back and think about what you’ve learned. What do these categories and patterns mean? What is really important

  • What did you learn?

Data Analysis and Interpretation

Data analysis consists of:

examining, categorizing, tabulating, testing, or otherwise recombining both quantitative and qualitative evidence to address the initial propositions of a study.


Interpretation and Analysis

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Data Analysis and Interpretation __ case study

making a matrix of categories

flow charts and other graphics

calculating relationships __ means and variances

putting information in a chronological order


Interpretation and Analysis

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Interpretation and Analysis __quant.

Reliability may be calculated by using Cohen's Kappa, which approaches 1 as coding is perfectly reliable and goes to 0 when there is no agreement other than what would be expected by chance (Haney et al., 1998).

Kappa is computed as:



Interpretation and Analysis __quant.

In addition, Landis & Koch (1977, p.165) have suggested the following benchmarks for interpreting kappa:

Typical errors

  • Listing all narrative comments without doing any analysis

  • Using quotes to provide a positive spin. Consider your purpose for including quotes.

Quantitative Methods _ defence, security and international politics

Programme content

The critical analysis of political ideas and global politics is a central theme of the degree course and you will examine a variety of theories and empirical evidence that confront contemporary and historical issues in international relations.

There will be a particular emphasis on training in Quantitative Methods that will enable you to engage more fully with opinion surveys, government statistics, large data sets, and other aspects of the fast-developing digital society.

These quantitative research skills, coupled with rigorous academic training in the discipline of MDIP, are increasingly required in today’s global job market.




Positivism

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Questions tackled when you study Defence, Security, Political Systems, Political Economy, international Studies and Quantitative Methods include:

  • What is power, who has it, and how is it used? How might we capture it empirically?
  • What is terrorism and how does it threaten our security?

  • What are rights and who do they belong to?
  • Why do states use violence against each other? How might measure violence globally?




Positivism

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Questions tackled when you study Defence, Security, Political Systems, Political Economy, international Studies and Quantitative Methods include:

  • How has globalisation affected patterns of inequality and justice? How can we use data to expose these patterns of inequality and injustice?

  • What is the political relationship between states and markets?

  • How can we use statistical information to understand these issues?

http://www2.warwick.ac.uk/fac/soc/pais/study/studyundergrad/polintqm/




Positivism

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Positivism

A trend in bourgeois philosophy which declares natural (empirical) sciences to be the sole source of true knowledge and rejects the cognitive value of philosophical study.

Positivism declared false and senseless all problems, concepts and propositions of traditional philosophy on being, substances, causes., etc., that could not be solved or verified by experience due to a high degree of abstract nature.




Positivism

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Positivism __ origin

Positivism was founded by Auguste Comte, who introduced the term "positivism.”

The exponents of the first were Comte, E. Littré and P. Laffitte in France, J S Mill and Herbert Spencer in England.

Alongside the problems of the theory of knowledge (Comte) and logic (Mill), the main place in the first Positivism was assigned to sociology (Comte's idea of transforming society on the basis of science, Spencer's organic theory of society).




Positivism

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Positivism

Positivism is also depicted as the view that all true knowledge is scientific, and that all things are ultimately measurable.

Positivism is closely related to reductionism, in that both involve the view that "entities of one kind... are reducible to entities of another," such as societies to numbers, or mental events to chemical events.



Positivism

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Positivism

Stephen Hawking has been regarded by some as an advocate of modern positivism, at least in the physical sciences. In The Universe in a Nutshell (p. 31) he writes:

“Any sound scientific theory, whether of time or of any other concept, should in my opinion be based on the most workable philosophy of science: the positivist approach put forward by Karl Popper and others. According to this way of thinking, a scientific theory is a mathematical model that describes and codifies the observations we make.”




Positivism

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Positivism

Criticism

Max Horkheimer and other critical theorists criticized positivism on two grounds.

First, it falsely represented human social action.

The first criticism argued that positivism systematically failed to appreciate the extent to which the so-called social facts it yielded did not exist 'out there', in the objective world, but were themselves a product of socially and historically mediated human consciousness.




Positivism

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Positivism

Criticism

Positivism ignored the role of the 'observer' in the constitution of social reality and thereby failed to consider the historical and social conditions affecting the representation of social ideas.

In recent decades, increasing attention is falling on the limitations of the epistemological base of positivism. Within positivism, knowledge has been treated as follows:

  • What counts is the means (methodology) by which knowledge is arrived at. These means must be objective, empirical and scientific;
  • Only certain topics are worthy of enquiry, namely those that exist in the public world;



Positivism

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Positivism

Criticism

The relationship between the self and knowledge has been largely denied – knowledge is regarded as separate from the person who constructs it. The political is separate from the personal;

Math's, science and technical knowledge are given high status, because they are regarded as objective, separate from the person and the private world;

Knowledge is construed as being something discovered, not produced by human beings.


Positivism

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Post - Positivism

Post-positivist research principles emphasize meaning and the creation of new knowledge, and are able to support committed social movements, that is, movements that aspire to change the world and contribute towards social justice.

Post-positivist research has the following characteristics:

  • Research is broad rather than specialized – lots of different things qualify as research;
  • Theory and practice cannot be kept separate. We cannot afford to ignore theory for the sake of ‘just the facts’;



Positivism

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Subject matter of lecture: Science and Research __ Quantitative research

Quantitative research methods were originally developed in the natural sciences to study natural phenomena.

However examples of quantitative methods now well accepted includes:

  • surveys;
  • laboratory experiments;
  • formal methods such as econometrics:
  • numerical methods such as mathematical modelling.



Quantitative Research



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Subject matter of lecture: Science and Research __ Quantitative research

Quantification can be useful, because it can

■ provide a broad familiarity with cases;

■ examine patterns across many cases;

show that a problem is numerically significant;

■ often be used as the starting point for a qualitative study;

■ provide readily available and unambiguous information.



Quantitative Methods



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Subject matter of lecture: Science and Research __ Quantitative research

Quantitative research has positivist features when it:

■ tries to link variables (features which vary from person to person);

tries to test theories or hypotheses;

■tries to predict;

■ tries to isolate and define categories before research starts and then to determine the relationships between them.



Quantitative Methods



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Subject matter of lecture: Science and Research __ Quantitative research

Quantitative research:

Procedures are standard, and replication is assumed

Analysis proceeds by using statistics, tables, or charts and discussing how what they show relates to hypotheses



Quantitative Methods



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Subject matter of lecture: Statistical Concepts

Variables __ dependent or independent

We may collect data concerning many variables, perhaps through a questionnaire, or choose to measure just two or several variables by observation or testing.

The variables we are interested in may be dependent or independent.



Quantitative Methods



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Subject matter of lecture: Statistical Concepts

Data

Using the data that you have collected then you can:

Describe variables in terms of distribution:

In fact univariate (one variable) analysis can only be descriptive.

But descriptive statistics can be used to describe a significant relationship between two variables (bivariate data) or more variables (multivariate).



Quantitative Methods



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Subject matter of lecture: Statistical Concepts

Basic measures

mean:

is a measure of the central location or average of a set of numbers, e.g. the mean of 2 7 2 1 8 2 6 9 10 5 1 4 is 4.75

standard deviation:

is the square root of the variance!!



Quantitative Methods



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Subject matter of lecture: Statistical Concepts

Basic measures

variance:  

is a measure of dispersion (or spread) of a set of data calculated in the following way:   




Quantitative Methods



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Subject matter of lecture: Statistical Concepts

Basic measures

median:

is the centre or middle number of a data set, e.g. the median of 2 7 2 1 8 2 6 9 10 5 1 4 is 4.5



Quantitative Methods



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Subject matter of lecture: Statistical Concepts

Basic measures

quartiles:

Divide a distribution of values into four equal parts.

The three corresponding values of the variable are denoted by Q1, Q2 (equal to the median) and Q3



Quantitative Methods



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Subject matter of lecture: Statistical Concepts

Basic measures

range:

is a measure of dispersion equal to the difference between the largest and smallest value.



Quantitative Methods



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Levels of Measurement

Variables may be measured or represented numerically in different ways:

Nominal

Level data represents categories with no inherent order. The numbers of a nominal level variable represent just codes to identify different categories e.g.. 1 = male 2 = female

Interval

Level data uses numbers to represent full arithmetic properties, such as weight in kilograms, income in dollars/Cedis, etc.



Quantitative Methods


Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Variables: Levels of Measurement

Variables may be measured or represented numerically in different ways:

Ordinal

Level data or scales represent a level of measurement between nominal and interval. The numbers used contain information about a rank ordering (1 = poor, 2 = average, 3 = rich) but the variable nature of the categories permit no normal mathematical operations.



Quantitative Methods


Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Scales of Measurement

Variables: Levels of Measurement

Level of measurement of variables is an extremely important concept in quantitative analysis:

The level of measurement of a variable determines what types of statistical procedures and analyses can be performed on it.

The higher the level of measurement, the wider the range of analyses which may be conducted on the data.

Thus, a variable collected at an interval level has a more flexibility than if it were collected at the nominal level. Furthermore, one can always recode data to a lower level (interval to ordinal to nominal) but never to a higher level.


Quantitative Methods


Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Sources of Data

Data is commonly classified as either primary or secondary.

Primary data is collected by or on behalf of the investigator and is specifically geared towards a specific need.

Secondary data is obtained from another agency or researcher and used for analysis.

It may not be specifically designed to address the issues(s) under investigation, either in terms of sample design or the variables collected and thus commonly requires some trade-off’s in terms of the questions which may be addressed.


Quantitative Methods


Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Cartesian coordinate system


Quantitative Methods


Cartesian coordinate system with the circle of radius 2 centered at the origin marked in red. The equation of the circle is x2 + y2 = 4.

Y = Dependent variable

X = Independent variable

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Cartesian coordinate system



Quantitative Methods


A three dimensional Cartesian coordinate system, with origin O and axis lines X, Y and Z, oriented as shown by the arrows. The tic marks on the axes are one length unit apart. The black dot shows the point with coordinates X = 2, Y = 3, and Z = 4, or (2,3,4).

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Formal Descriptive Statistics

Frequency Table

A frequency table is constructed by dividing the scores into intervals and counting the number of scores in each interval. The actual number of scores as well as the percentage of scores in each interval are displayed.

Cumulative frequencies are also usually displayed. A frequency table for the tournament players from the example dataset "chess" is shown below:



Quantitative Methods


Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Formal Descriptive Statistics



Quantitative Methods


Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Histogram

A histogram is constructed from a frequency table.

The intervals are shown on the X-axis and the number of scores in each interval is represented by the height of a rectangle located above the interval.

A histogram of the response times from the dataset Target RT is shown below.

The shapes of histograms will vary depending on the choice of the size of the intervals.



Quantitative Methods


Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Histogram


Quantitative Methods

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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A Bar Graph

A bar graph is much like a histogram, differing in that the columns are separated from each other by a small distance.

Bar graphs are commonly used for qualitative variables. Qualitative variables are

sometimes called "categorical variables.“

Quantitative variables are measured on an ordinal, interval, or ratio scale; qualitative variables are measured on a nominal scale.

If five-year old subjects were asked to name their favorite color, then the variable would be qualitative. If the time it took them to respond were measured, then the variable would be quantitative.



Quantitative Methods


Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Source: https://www.google.co.uk/search?q=greek+letters&newwindow=1&rlz=1C1SKPM_enGH499GH505&espv=210&es_sm=122&tbm=isch&tbo=u&source=univ&sa=X&ei=dboiU73NBKuBywOGzoLoCw&ved=0CDgQsAQ&biw=1316&bih=615#facrc=_&imgdii=_&imgrc=upeV0njO2SuPoM%253A%3B8NGcq9zXZ9jWdM%3Bhttp%253A%252F%252F0.tqn.com%252Fd%252Fgogreece%252F1%252F0%252Fu%252Fn%252FGreek-Alphabet-Chart-Letters.JPG%3Bhttp%253A%252F%252Fgogreece.about.com%252Fod%252Fgreeklanguage2%252Fss%252Fgreekalphabet_9.htm%3B345%3B350

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Measures of Central Tendency

Measures of central tendency define values that lie centrally within a data set organized in order of magnitude. The term refers to the ‘’middle value’’ or perhaps a typical value of the data, and is measured using the mean, median, or mode. Each of these measures is calculated differently, and the one that is best to use depends upon the situation.

In contrast, measures of dispersion describe the extent of variation within a data set.



Quantitative Methods


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Measures of Central Tendency

Measures of central tendency define values that lie centrally within a data set organized in order of magnitude. In contrast, measures of dispersion describe the extent of variation within a data set.

Arithmetic Mean

Can be thought as the ‘’center of gravity’’ of a set of data.

x = Σn (1) mean for samples

n


Quantitative Methods


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Measures of Central Tendency

Arithmetic Mean

Can be thought as the ‘’center of gravity’’ of a set of data.

µ = ΣX (1) mean for population

N


Quantitative Methods


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Parametric and Non – parametric Statistics

Most commonly used statistical techniques are properly called parametric

because they involve estimating or testing the value(s) of parameter(s)--usually,

population means or proportions.

It should come as no surprise, then, that non-parametric methods are

procedures that work their magic without reference to specific parameters.

The precise definition of non-parametric varies slightly among authors. You'll

see the terms non-parametric and distribution-free. They have slightly different

meanings, but are often used interchangeably.



Quantitative Methods


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Quantitative Methods


Some Commonly Used Statistical Tests
Normal theory based test Corresponding nonparametric test Purpose of test
t test for independent samples Mann-Whitney U test; Wilcoxon rank-sum test Compares two independent samples
Paired t test Wilcoxon matched pairs signed-rank test Examines a set of differences
Pearson correlation coefficient Spearman rank correlation coefficient Assesses the linear association between two variables.
One way analysis of variance (F test) Kruskal-Wallis analysis of variance by ranks Compares three or more groups
Two way analysis of variance Friedman Two way analysis of variance Compares groups classified by two different factors

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Parametric and Non – parametric Statistics

Some non-parametric procedures:

Mann-Whitney test__

The Wilcoxon rank sum test (also known as the Mann-Whitney U test or the

Wilcoxon-Mann-Whitney test) is used to test whether two samples are drawn from

the same population.

For large samples, the statistic is compared to percentiles of the standard

normal distribution. For small samples, the statistic is compared to what would

result if the data were combined into a single data set and assigned at random to

two groups having the same number of observations as the original samples.



Quantitative Methods


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Parametric and Non – parametric Statistics

Some parametric procedures:

The T-Test

The t-test assesses whether the means of two groups are statistically different from each other. This analysis is appropriate whenever you want to compare the means of two groups.

Example:

Medical trial __ 1: treatment group 2: control group



Quantitative Methods


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  • Figure 1 shows the distributions for the

treated (blue) and control (green) groups in a

study.

The figure indicates where the control and treatment group means are located.

The question the t-test addresses is whether the means are statistically different.



Quantitative Methods


Figure 1. Idealized distributions for treated and comparison group post-test values.

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Question? _So how do we know whether the effect observed in our sample was genuine?

  • We don’t
  • Instead we use p values to indicate our level of certainty that our results represent a genuine effect present in the whole population

the z-score represents a value on the x-axis for which we know the p-value (the p- value measures consistency between the results actually obtained in the trial and pure chance explanation for those results).

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Q

Explain that by genuine we mean that the observed effect was caused by a true effect present within the whole population

A

P values

P values = the probability that the observed result was obtained by chance

  • i.e. when the null hypothesis is true

α level is set a priori (Usually 0.05)

If p < α level then we reject the null hypothesis and accept the experimental hypothesis

  • 95% certain that our experimental effect is genuine

If however, p > α level then we reject the experimental hypothesis and accept the null hypothesis

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P = Probability

this value tells us the probability that the observed result was obtained by chance

That there is no difference between the two groups

Each test result (e.g. t value) is associated with a particular p value

α level is set a priori

This is basically an acceptance level

Usually this is set to 0.05

But as I understand, α levels are usually much lower than this in fMRI

If p < α level then we reject the null hypothesis and accept the experimental hypothesis

- concluding that we are 95% certain that our experimental effect is genuine

If however, p > α level then we reject the experimental hypothesis and accept the null hypothesis

- that there was no sig diff in brain activation levels between the two conditions

Types of Errors

There are two types of error that you can make:

A Type 1 (or alpha) error denotes a false positive result, i.e. that you accept the H1 in your data, even though the H0 is true

Conversely, a type 2 (or beta) error denotes a false negative result, i.e. that you accept the H0, even though the H1 is true

The two green fields describe the remaining probability that, given alpha (or beta), you are making the correct decision when you accept the H0 (true negative result) or reject the H0 (i.e. accept the H1) (true positive result)

The way in which we decide whether a given value is highly unlikely (i.e. Statistically significant) is to look at the underlying distribution

Population
H0 H1
Sample H0 1-a b-error (Type II error)
H1 a-error (Type I error) 1-b

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Type I Error

H0 ------true

But we reject H0

Example: Innocent but found guilty.

  • Type II Error

  • H0 ------false
  • But we fail to reject H0

Example: Guilty but food innocent

Two types of errors

Statistical Analysis of the t-test

The formula for the t-test is a ratio.

The top part of the equation is the

difference between the two means.

The bottom part is a measure of the

variability or dispersion of the scores.

The variability is essentially "noise"

that may make it harder for us to see

group differences. The formula for the t-

test is:



Quantitative Methods


Group 1 mean - Group 2 mean/

Standard Error (Group 1 mean-Group 2 mean).

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Statistical Analysis of the t-test

The bottom part of the formula is called the standard error of the difference.

To compute it, we take the variance (the standard deviation squared) for each group and divide it by the number of respondents in that group minus 1.

We add these two values, then take their square root. The specific formula is shown as:



Quantitative Methods


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Statistical Analysis of the t-test

The final combined formula for the t-

test is:

Note:

The t-value will be positive if the mean

from Group I is larger than the mean of group II and negative if it is smaller.

Once you compute the t-value you look up the t-value in a table of significance which tells us whether the ratio is large enough to say that the difference between the groups is significant. 


Quantitative Methods


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Statistical Analysis of the t-test

The final combined formula for the t-

test is:

Note:

In other words the difference observed

is not likely due to chance or sampling error.

As with any test of significance, you need to set the alpha level.

In most research, the "rule of thumb" is to set the alpha level at .05. This means that 5% of the time (five times out of a hundred) you would find a statistically significant difference between the means even if there is none ("chance").



Quantitative Methods


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Statistical Analysis of the t-test

The final combined formula for the t-test is:

Note:

The t-test also requires that we determine the degrees of freedom (df) for the test. In the t-test, the degrees of freedom is the sum of the persons in both groups minus 2.

Given the alpha level, the df, and the t-value, you can look the t-value up in a standard table of significance (available as an appendix in the back of most statistics texts) to determine whether the t-value is large enough to be significant.



Quantitative Methods


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Degrees of freedom (df)

  • Number of scores in a sample that are free to vary
  • n=4 scores; mean=10

Formula: df=n-1

 4-1 =3

So df is 3

Statistical Analysis of the t-test

The final combined formula for the t-test is:

Note:

If it is, you can conclude that the difference between the means for the two groups is different.

Statistical computer programs routinely print the significance test results, saving you from looking them up in a table.


Quantitative Methods


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Some parametric procedures:

The T-Test

The t-test is a parametric test.  In order to use a t-test, several assumptions

must be met. The further away from meeting these assumptions that we get, the less

reliable the test statistic becomes. The assumptions are:

observations are independent and not paired

observations for each group are a sample from a population that is normally distributed

variances for the two independent groups must be considered in interpretation of the test statistic


Quantitative Methods

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The Logic and the Process of Analysis of Variance (ANOVA)

  • The purpose of ANOVA is much the same as the t tests presented in the preceding slides:

the goal is to determine whether the mean differences that are obtained for sample data are sufficiently large to justify a conclusion that there are mean differences between the populations from which the samples were obtained.

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The Logic and the Process of Analysis of Variance (cont.)

The difference between ANOVA and the t tests is that ANOVA can be used in situations where there are two or more means being compared, whereas the t tests are limited to situations where only two means are involved.

Analysis of variance is necessary to protect researchers from excessive risk of a Type I error in situations where a study is comparing more than two population means.

ANOVA

Reminders: A factor is a variable that can take one of several levels used to differentiate one group from another.

An experiment has a one-way, or completely randomized, design if several levels of one factor are being studied and the individuals are randomly assigned to its levels. (There is only one way to group the data.)

Therefore, Analysis of variance (ANOVA) is the technique used to determine whether more than two population means are equal.

One-way ANOVA is used for completely randomized, one-way designs.

What does ANOVA test?

  • The null hypothesis tests whether the mean of all the independent samples is equal

H0 1= 2 = 3 …..= n

H1 1 2  3 …..  n

  • The alternative hypothesis specifies that all the means are not equal

NB:

Alternative hypothesis __ HA or H1

Null hypothesis __ HO or H2

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F Statistic

  • Like any other test, the ANOVA test has its own test statistic

The statistic for ANOVA is called the F statistic, which we get from the F Test

  • The F statistic takes into consideration:

number of samples taken (I)

sample size of each sample (n1, n2, …, nI)

means of the samples ( 1, 2, …, I)

standard deviations of each sample (s1, s2, …, sI)

F Statistic Equation

Rewritten as a formula, the F Statistic looks like this:

Weighing

Weighing

Standard Deviations (Squared)

Means (Squared)

One-Tailed Versus Two-Tailed Tests

The form of the alternative hypothesis can be either a one-tailed or two-tailed, depending on what you are trying to prove.

One tailed hypothesis __ A one-tailed hypothesis is one where the only sample results which can lead to rejection of the null hypothesis are those in a particular direction, namely, those where the sample mean rating is positive.

A two-tailed test __ A two-tailed test is one where results in either of two directions can lead to rejection of the null hypothesis.

Formulate H1and H0

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One-Tailed Versus Two-Tailed Tests -- continued

One tailed alternatives are phrased in terms of “>” or “<“ whereas two tailed alternatives are phrased in terms of “”

The real question is whether to set up hypotheses for a particular problem as one-tailed or two-tailed.

There is no statistical answer to this question. It depends entirely on what we are trying to prove.

Formulate H1and H0

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Example

As the manager you would like to observe a difference between both pizzas (Old and New).

  • If the new baking method is cheaper, you would like the preference to be for it.
  • Null Hypothesis

  • Alternative

Two tail

test

One tail

test

H0 =0 (there is no difference between the old style and the new style pizzas)

The difference between the mean of the sample and the mean of the population is zero.

H1 0 or H1  >0

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Select Appropriate Test

What we want to test is whether consumers prefer the new style pizza to the old style. We assume that there is no difference (i.e. the mean of the population is zero) and want to know whether our observed result is significantly (i.e. statistically) different.

The one-sample t test is used to test whether the mean of the sample is equal to a hypothesized value of the population from which the sample is drawn.

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Choose Level of Significance

Significance Level selected is typically .05 or .01

i.e 5% or 1%

1 – 0.05 = 95%

1- 0.01 = 99%

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The ratings of 40 randomly selected customers produces the following table and statistics

From the summary statistics, we see that the sample mean is 2.10 and the sample standard deviation is 4.717

The positive sample mean suggests a slight preference for the new pizza, (alternative hypothesis) but there is a fair degree of variation. What we don’t know is whether this preference is significant

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Calculate the Test Statistic

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Determine the Probability-value (Critical Value)

We use the right tail because the alternative is one-tailed of the “greater than” variety

The probability beyond this value in the right tail of the t distribution with n-1 = 39 degrees of freedom is approximately 0.004

The probability, 0.004, is the p-value for the test. It indicates that these sample results would be very unlikely if the null hypothesis is true.

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1-

Reject H0

Reject H0

Do not Reject H0

Compare with the level of significance,  (.05)and determine if the critical value falls in the rejection region

Since the statistic falls in the rejection area we reject Ho and conclude that the perceived difference between the pizzas is significantly different from zero.

Reject or do not reject H0

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Conclusion

  • the sample evidence is fairly convincing that customers, on average, prefer the new-style pizza.
  • Should the manager switch to the new-style pizza on the basis of these sample results?
  • Depends. There is no indication that the new-style pizza costs any more to make than the old-style pizza. Therefore, unless there are reasons for not switching (for example, costs) then we recommend the switch.

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Excel output for the one-way ANOVA

numerator

denominator

Here, the calculated F-value (12.08) is larger than Fcritical (3.49) for a = 0.05.

Thus, the test is significant at a = 5%

ANOVA

BPS 10.17 ANOVA F test
Do nematodes affect plant growth? A botanist prepares 16 identical planting pots and
adds different numbers of nematodes into the pots. Seedling growth is recorded 16 days later.
Seedling growth x bar i overall x bar s i
0 nematode 10.8 9.1 13.5 9.2 10.65 8.03125 2.0534523775
1000 nematodes 11.1 11.1 8.2 11.3 10.425 8.03125 1.486326568
5000 nematodes 5.4 4.6 7.4 5 5.6 8.03125 1.2436505404
10000 nematodes 5.8 5.3 3.2 7.5 5.45 8.03125 1.7710637105
Hypotheses all mu the same vs. not all mu the same
Conditions required s i max no more than s i min; distributions "roughly" normal
MSG = mean square for groups ( = SSG / df groups)
MSE = mean square for errors ( = SSE / df overall)
F = MSG = variation among sample means
MSE variation among individuals in same sample
MSG = sum ( ni(x bar i - x bar)^2 )
I -1 Df = I -1
I is the number of samples/groups/conditions compared
MSE = sum ( (ni -1)si^2 ) N is the total number of measurments across all samples
N - I Df = N - I
Menu/Tools/DataAnalysis/AnovaSingleFactor
Anova: Single Factor
SUMMARY
Groups Count Sum Average Variance
0 nematode 4 42.6 10.65 4.2166666667
1000 nematodes 4 41.7 10.425 2.2091666667
5000 nematodes 4 22.4 5.6 1.5466666667
10000 nematodes 4 21.8 5.45 3.1366666667
ANOVA
Source of Variation SS df MS F P-value F crit
Between Groups 100.646875 3 33.5489583333 12.0797389543 0.0006162913 3.4902996049
Within Groups 33.3275 12 2.7772916667
Total 133.974375 15
&L&F - &A - &D&RBio7 s03 - Baldi Discussions

ANOVA

seedling growth

Review

Review of concepts Estimating the parameter mu Estimating the parameter p
(population mean, quatitative variable) (population proportion, categorical)
1 sample sigma known: sigma unknown:
variable x_bar x_bar p_hat
distibution z distribution t distribution z distribution
Ho value of mu under Ho value of mu under Ho value of p under Ho
spread sigma/sqrt(n) (s/sqrt(n)) sqrt(p(1-p)/n)
deg. freedom df = n - 1
2 samples, sigma known: sigma unknown:
paired variable x_bar for difference x_bar for difference
distibution z distribution t distribution Not applicable,
Ho mu difference = 0 mu difference = 0 Categorical data
spread sigma/sqrt(n) s/sqrt(n)
deg. freedom df = n - 1
2 samples, variable x_bar1 - x_bar2 p_hat1 - p_hat2
independt distribution t distribution z distribution
Ho mu1 - mu2 = 0 p1 - p2 = 0
spread sqrt( s1^2/n1 + s2^2/n2) sqrt[ p1(1-p1)/n1 + p2(1-p2)/n2 ]
deg. freedom df aprox. smallest(n1, n2) - 1 use p_hat1 and p_hat2 for estimating
>2 samples distribution F distribution - ANOVA Chi square distribution
( F = MSG / MSE ) ( X2 = sum (expect - actual)^2/expect )
Ho all mu are the same (alt. not all the same) all p are the same (alt. not all the same)
deg. freedom df numer = I - 1 (I: # samples compared) df = (r - 1)(c - 1)
df denom = N - I (N: # measurments)
SSG
SSE
MSG
MSE
Df numerator
Df denominator
F* corresponding to alpha 0.05 for the 2 degrees of freedom 3 and 12
&L&F - &A - &D&RBio7 s03 - Baldi Discussions

Exercises

What test do you need to perform? Final review
What are your hypotheses?
What are the characteristics of the distribution you will use?
* During an angiogram, heart problems can be examined using a catheter threaded into the heart from a vein in the patient's leg. The manufacturer
must maintain a diameter of 2 mm for the catheters (manufacturing stdev 0.1 mm). Random samples of 5 catheters are taken and measured daily.
quantitative; sigma known; 1 sample z test (no df) Ho: mu = 2 mm; Ha: mu ≠ 2 mm (2 sided)
* A researcher wants to see if there is a significant difference in Resting Pulse Rates for men and women.
For the study, 28 men and 24 women had their pulse rate measured at rest in the lab.
quantitative; indept samples; 2 sample t test, df 23 Ho: mu men - mu women = 0 (vs. ≠0, 2 sided)
* The Journal of Applied Psychology reported in 2002 a study testing whether the content of TV shows influences the ability of
viewers to recall brands presented during commercials. Volunteers were randomly assigned to watch 1 of 3 programs, each
containing the same 9 commercials. One program had violent content, another sexual content, and the other one neutral content.
After the shows, subjects (20 in each group) were tested to see how many brands they could recall.
quantitative; anova, dfn 2 dfd 57 (N = 60, I = 3) Ho: mu violent = mu sexual = mu neutral
* Does ginkgo biloba enhance memory? Subjects were assigned randomly to take either ginkgo biloba suplements (35 subjects)
or a placebo (30 subjects). Their memory was later tested to see if it had improved.
quantitative; indept samples; 2 sample t test, df 29 Ho: mu improv GB - mu improv Pl = 0 (vs. >0, 1 sided)
* Common folk wisdom has it that drinking cranberry juice helps prevent urinary track infection in women. A study published in the
British Medical Journal in 2001 tested women assigned randomly to 1 of 3 groups: daily intake of cranberry juice, daily intake
of lactobacillus drink, or neither of these 2 drinks. The proportion of women developing at least 1 urinary trackt infection during the
6 month study was recorded for each group (50 women in each group).
categorical; chi square, df 2 (3 by 2 table) Ho: p cranb. = p lactob. = p neither
* Cuckoos lay their eggs in the nests of other bird species which will take on the burden of parenting the parasite offspring.
Cuckoos parasite several bird species, all laying eggs of different sizes. Do cuckoos change the size of their eggs to mimic that
of the bird species in which the eggs are laid? Cuckoo egg length were measured for eggs that had been laid in nests of either
sparrow (14 eggs), robin (16 eggs) or wagtail (15 eggs) host bird species.
quantitative; anova, dfn 2 dfd 42 (N = 45, I = 3) Ho: mu sparrow = mu robin = mu wagtail
* Many dairy cows now receive injections of BST, a hormone intended to spur greater milk production. The milk production of 60
Ayrshire dairy cows was recorded before and after they received a first injection of BST.
quantitative; match pair design; paired t-test, df 59 Ho: mu difference = 0 (vs. >0, 1 sided)
* Medical researchers followed 6272 Swedish men for 30 years to see if there was any association between the amount of fish
in their diet (never/selcom, small, moderate, large part of the diet) and prostate cancer.
categorical; chi square, df 3 (4 by 2 table) Ho: p seldom = p small = p moderate = p large
&L&F - &A - &D&RBio7 s03 - Baldi Discussions

Table A

z 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09
-3.4 0.0003 0.0003 0.0003 0.0003 0.0003 0.0003 0.0003 0.0003 0.0003 0.0002
-3.3 0.0005 0.0005 0.0005 0.0004 0.0004 0.0004 0.0004 0.0004 0.0004 0.0003
-3.2 0.0007 0.0007 0.0006 0.0006 0.0006 0.0006 0.0006 0.0005 0.0005 0.0005
-3.1 0.0010 0.0009 0.0009 0.0009 0.0008 0.0008 0.0008 0.0008 0.0007 0.0007
-3 0.0013 0.0013 0.0013 0.0012 0.0012 0.0011 0.0011 0.0011 0.0010 0.0010
-2.9 0.0019 0.0018 0.0018 0.0017 0.0016 0.0016 0.0015 0.0015 0.0014 0.0014 Table of areas under the
-2.8 0.0026 0.0025 0.0024 0.0023 0.0023 0.0022 0.0021 0.0021 0.0020 0.0019 Normal distribution curve
-2.7 0.0035 0.0034 0.0033 0.0032 0.0031 0.0030 0.0029 0.0028 0.0027 0.0026 to the left of z values
-2.6 0.0047 0.0045 0.0044 0.0043 0.0041 0.0040 0.0039 0.0038 0.0037 0.0036
-2.5 0.0062 0.0060 0.0059 0.0057 0.0055 0.0054 0.0052 0.0051 0.0049 0.0048
-2.4 0.0082 0.0080 0.0078 0.0075 0.0073 0.0071 0.0069 0.0068 0.0066 0.0064
-2.3 0.0107 0.0104 0.0102 0.0099 0.0096 0.0094 0.0091 0.0089 0.0087 0.0084
-2.2 0.0139 0.0136 0.0132 0.0129 0.0125 0.0122 0.0119 0.0116 0.0113 0.0110
-2.1 0.0179 0.0174 0.0170 0.0166 0.0162 0.0158 0.0154 0.0150 0.0146 0.0143
-2 0.0228 0.0222 0.0217 0.0212 0.0207 0.0202 0.0197 0.0192 0.0188 0.0183
-1.9 0.0287 0.0281 0.0274 0.0268 0.0262 0.0256 0.0250 0.0244 0.0239 0.0233
-1.8 0.0359 0.0351 0.0344 0.0336 0.0329 0.0322 0.0314 0.0307 0.0301 0.0294
-1.7 0.0446 0.0436 0.0427 0.0418 0.0409 0.0401 0.0392 0.0384 0.0375 0.0367
-1.6 0.0548 0.0537 0.0526 0.0516 0.0505 0.0495 0.0485 0.0475 0.0465 0.0455
-1.5 0.0668 0.0655 0.0643 0.0630 0.0618 0.0606 0.0594 0.0582 0.0571 0.0559
-1.4 0.0808 0.0793 0.0778 0.0764 0.0749 0.0735 0.0721 0.0708 0.0694 0.0681
-1.3 0.0968 0.0951 0.0934 0.0918 0.0901 0.0885 0.0869 0.0853 0.0838 0.0823
-1.2 0.1151 0.1131 0.1112 0.1093 0.1075 0.1056 0.1038 0.1020 0.1003 0.0985
-1.1 0.1357 0.1335 0.1314 0.1292 0.1271 0.1251 0.1230 0.1210 0.1190 0.1170
-1 0.1587 0.1562 0.1539 0.1515 0.1492 0.1469 0.1446 0.1423 0.1401 0.1379
-0.9 0.1841 0.1814 0.1788 0.1762 0.1736 0.1711 0.1685 0.1660 0.1635 0.1611
-0.8 0.2119 0.2090 0.2061 0.2033 0.2005 0.1977 0.1949 0.1922 0.1894 0.1867
-0.7 0.2420 0.2389 0.2358 0.2327 0.2296 0.2266 0.2236 0.2206 0.2177 0.2148
-0.6 0.2743 0.2709 0.2676 0.2643 0.2611 0.2578 0.2546 0.2514 0.2483 0.2451
-0.5 0.3085 0.3050 0.3015 0.2981 0.2946 0.2912 0.2877 0.2843 0.2810 0.2776
-0.4 0.3446 0.3409 0.3372 0.3336 0.3300 0.3264 0.3228 0.3192 0.3156 0.3121
-0.3 0.3821 0.3783 0.3745 0.3707 0.3669 0.3632 0.3594 0.3557 0.3520 0.3483
-0.2 0.4207 0.4168 0.4129 0.4090 0.4052 0.4013 0.3974 0.3936 0.3897 0.3859
-0.1 0.4602 0.4562 0.4522 0.4483 0.4443 0.4404 0.4364 0.4325 0.4286 0.4247
-0.0 0.5000 0.4960 0.4920 0.4880 0.4840 0.4801 0.4761 0.4721 0.4681 0.4641
0.0 0.5000 0.5040 0.5080 0.5120 0.5160 0.5199 0.5239 0.5279 0.5319 0.5359
0.1 0.5398 0.5438 0.5478 0.5517 0.5557 0.5596 0.5636 0.5675 0.5714 0.5753
0.2 0.5793 0.5832 0.5871 0.5910 0.5948 0.5987 0.6026 0.6064 0.6103 0.6141
0.3 0.6179 0.6217 0.6255 0.6293 0.6331 0.6368 0.6406 0.6443 0.6480 0.6517
0.4 0.6554 0.6591 0.6628 0.6664 0.6700 0.6736 0.6772 0.6808 0.6844 0.6879
0.5 0.6915 0.6950 0.6985 0.7019 0.7054 0.7088 0.7123 0.7157 0.7190 0.7224
0.6 0.7257 0.7291 0.7324 0.7357 0.7389 0.7422 0.7454 0.7486 0.7517 0.7549
0.7 0.7580 0.7611 0.7642 0.7673 0.7704 0.7734 0.7764 0.7794 0.7823 0.7852
0.8 0.7881 0.7910 0.7939 0.7967 0.7995 0.8023 0.8051 0.8078 0.8106 0.8133
0.9 0.8159 0.8186 0.8212 0.8238 0.8264 0.8289 0.8315 0.8340 0.8365 0.8389
1.0 0.8413 0.8438 0.8461 0.8485 0.8508 0.8531 0.8554 0.8577 0.8599 0.8621
1.1 0.8643 0.8665 0.8686 0.8708 0.8729 0.8749 0.8770 0.8790 0.8810 0.8830
1.2 0.8849 0.8869 0.8888 0.8907 0.8925 0.8944 0.8962 0.8980 0.8997 0.9015
1.3 0.9032 0.9049 0.9066 0.9082 0.9099 0.9115 0.9131 0.9147 0.9162 0.9177
1.4 0.9192 0.9207 0.9222 0.9236 0.9251 0.9265 0.9279 0.9292 0.9306 0.9319
1.5 0.9332 0.9345 0.9357 0.9370 0.9382 0.9394 0.9406 0.9418 0.9429 0.9441
1.6 0.9452 0.9463 0.9474 0.9484 0.9495 0.9505 0.9515 0.9525 0.9535 0.9545
1.7 0.9554 0.9564 0.9573 0.9582 0.9591 0.9599 0.9608 0.9616 0.9625 0.9633
1.8 0.9641 0.9649 0.9656 0.9664 0.9671 0.9678 0.9686 0.9693 0.9699 0.9706
1.9 0.9713 0.9719 0.9726 0.9732 0.9738 0.9744 0.9750 0.9756 0.9761 0.9767
2.0 0.9772 0.9778 0.9783 0.9788 0.9793 0.9798 0.9803 0.9808 0.9812 0.9817
2.1 0.9821 0.9826 0.9830 0.9834 0.9838 0.9842 0.9846 0.9850 0.9854 0.9857
2.2 0.9861 0.9864 0.9868 0.9871 0.9875 0.9878 0.9881 0.9884 0.9887 0.9890
2.3 0.9893 0.9896 0.9898 0.9901 0.9904 0.9906 0.9909 0.9911 0.9913 0.9916
2.4 0.9918 0.9920 0.9922 0.9925 0.9927 0.9929 0.9931 0.9932 0.9934 0.9936
2.5 0.9938 0.9940 0.9941 0.9943 0.9945 0.9946 0.9948 0.9949 0.9951 0.9952
2.6 0.9953 0.9955 0.9956 0.9957 0.9959 0.9960 0.9961 0.9962 0.9963 0.9964
2.7 0.9965 0.9966 0.9967 0.9968 0.9969 0.9970 0.9971 0.9972 0.9973 0.9974
2.8 0.9974 0.9975 0.9976 0.9977 0.9977 0.9978 0.9979 0.9979 0.9980 0.9981
2.9 0.9981 0.9982 0.9982 0.9983 0.9984 0.9984 0.9985 0.9985 0.9986 0.9986
3.0 0.9987 0.9987 0.9987 0.9988 0.9988 0.9989 0.9989 0.9989 0.9990 0.9990
3.1 0.9990 0.9991 0.9991 0.9991 0.9992 0.9992 0.9992 0.9992 0.9993 0.9993
3.2 0.9993 0.9993 0.9994 0.9994 0.9994 0.9994 0.9994 0.9995 0.9995 0.9995
3.3 0.9995 0.9995 0.9995 0.9996 0.9996 0.9996 0.9996 0.9996 0.9996 0.9997
3.4 0.9997 0.9997 0.9997 0.9997 0.9997 0.9997 0.9997 0.9997 0.9997 0.9998

Table C

t* Upper tail probability P
df 0.25 0.2 0.15 0.1 0.05 0.025 0.02 0.01 0.005 0.0025 0.001 0.0005
1 1.000 1.376 1.963 3.078 6.314 12.710 15.890 31.820 63.660 127.300 318.300 636.600
2 0.816 1.061 1.386 1.886 2.920 4.303 4.849 6.965 9.925 14.090 22.330 31.600
3 0.765 0.978 1.250 1.638 2.353 3.182 3.482 4.541 5.841 7.453 10.210 12.920
4 0.741 0.941 1.190 1.533 2.132 2.776 2.999 3.747 4.604 5.598 7.173 8.610
5 0.727 0.920 1.156 1.476 2.015 2.571 2.757 3.365 4.032 4.773 5.893 6.869
6 0.718 0.906 1.134 1.440 1.943 2.447 2.612 3.143 3.707 4.317 5.208 5.959
7 0.711 0.896 1.119 1.415 1.895 2.365 2.517 2.998 3.499 4.029 4.785 5.408
8 0.706 0.889 1.108 1.397 1.860 2.306 2.449 2.896 3.355 3.833 4.501 5.041
9 0.703 0.883 1.100 1.383 1.833 2.262 2.398 2.821 3.250 3.690 4.297 4.781
10 0.700 0.879 1.093 1.372 1.812 2.228 2.359 2.764 3.169 3.581 4.144 4.587
11 0.697 0.876 1.088 1.363 1.796 2.201 2.328 2.718 3.106 3.497 4.025 4.437
12 0.695 0.873 1.083 1.356 1.782 2.179 2.303 2.681 3.055 3.428 3.930 4.318
13 0.694 0.870 1.079 1.350 1.771 2.160 2.282 2.650 3.012 3.372 3.852 4.221
14 0.692 0.868 1.076 1.345 1.761 2.145 2.264 2.624 2.977 3.326 3.787 4.140
15 0.691 0.866 1.074 1.341 1.753 2.131 2.249 2.602 2.947 3.286 3.733 4.073
16 0.690 0.865 1.071 1.337 1.746 2.120 2.235 2.583 2.921 3.252 3.686 4.015
17 0.689 0.863 1.069 1.333 1.740 2.110 2.224 2.567 2.898 3.222 3.646 3.965
18 0.688 0.862 1.067 1.330 1.734 2.101 2.214 2.552 2.878 3.197 3.611 3.922
19 0.688 0.861 1.066 1.328 1.729 2.093 2.205 2.539 2.861 3.174 3.579 3.883
20 0.687 0.860 1.064 1.325 1.725 2.086 2.197 2.528 2.845 3.153 3.552 3.850
22 0.686 0.858 1.061 1.321 1.717 2.074 2.183 2.508 2.819 3.119 3.505 3.792
25 0.684 0.856 1.058 1.316 1.708 2.060 2.167 2.485 2.787 3.078 3.450 3.725
30 0.683 0.854 1.055 1.310 1.697 2.042 2.147 2.457 2.750 3.030 3.385 3.646
40 0.681 0.851 1.050 1.303 1.684 2.021 2.123 2.423 2.704 2.971 3.307 3.551
50 0.679 0.849 1.047 1.299 1.676 2.009 2.109 2.403 2.678 2.937 3.261 3.496
60 0.679 0.848 1.045 1.296 1.671 2.000 2.099 2.390 2.660 2.915 3.232 3.460
80 0.678 0.846 1.043 1.292 1.664 1.990 2.088 2.374 2.639 2.887 3.195 3.416
100 0.677 0.845 1.042 1.290 1.660 1.984 2.081 2.364 2.626 2.871 3.174 3.390
1,000 0.675 0.842 1.037 1.282 1.646 1.962 2.056 2.330 2.581 2.813 3.098 3.300
z* 0.674 0.841 1.036 1.282 1.645 1.960 2.054 2.326 2.576 2.807 3.091 3.291
50% 60% 70% 80% 90% 95% 96% 98% 99% 99.50% 99.80% 99.90%
Confidence level C

Table D

F* Degrees of freedom (Df) in the numerator
Proba "p" 1 2 3 4 5 6 7 8 9 10 15 20 30 60 120 1000
Df den. 0.100 39.86 49.5 53.59 55.83 57.24 58.2 58.91 59.44 59.86 60.19 61.22 61.74 62.26 62.79 63.06 63.3
0.050 161.45 199.5 215.71 224.58 230.16 233.99 236.77 238.88 240.54 241.88 245.95 248.01 250.1 252.2 253.25 254.19
1 0.025 647.79 799.5 864.16 899.58 921.85 937.11 948.22 956.66 963.28 968.63 984.87 993.1 1001.4 1009.8 1014 1017.7
0.010 4052.2 4999.5 5403.4 5624.6 5763.6 5859 5928.4 5981.1 6022.5 6055.8 6157.3 6208.7 6260.6 6313 6339.4 6362.7
0.001 405284 500000 540379 562500 576405 585937 592873 598144 602284 605621 615764 620908 626099 631337 633972 636301
0.100 8.53 9 9.16 9.24 9.29 9.33 9.35 9.37 9.38 9.39 9.42 9.44 9.46 9.47 9.48 9.49
0.050 18.51 19 19.16 19.25 19.3 19.33 19.35 19.37 19.38 19.4 19.43 19.45 19.46 19.48 19.49 19.49
2 0.025 38.51 39 39.17 39.25 39.3 39.33 39.36 39.37 39.39 39.4 39.43 39.45 39.46 39.48 39.49 39.5
0.010 98.5 99 99.17 99.25 99.3 99.33 99.36 99.37 99.39 99.4 99.43 99.45 99.47 99.48 99.49 99.5
0.001 998.5 999 999.17 999.25 999.3 999.33 999.36 999.37 999.39 999.4 999.43 999.45 999.47 999.48 999.49 999.5
0.100 5.54 5.46 5.39 5.34 5.31 5.28 5.27 5.25 5.24 5.23 5.2 5.18 5.17 5.15 5.14 5.13
0.050 10.13 9.55 9.28 9.12 9.01 8.94 8.89 8.85 8.81 8.79 8.7 8.66 8.62 8.57 8.55 8.53
3 0.025 17.44 16.04 15.44 15.1 14.88 14.73 14.62 14.54 14.47 14.42 14.25 14.17 14.08 13.99 13.95 13.91
0.010 34.12 30.82 29.46 28.71 28.24 27.91 27.67 27.49 27.35 27.23 26.87 26.69 26.5 26.32 26.22 26.14
0.001 167.03 148.5 141.11 137.1 134.58 132.85 131.58 130.62 129.86 129.25 127.37 126.42 125.45 124.47 123.97 123.53
0.100 4.54 4.32 4.19 4.11 4.05 4.01 3.98 3.95 3.94 3.92 3.87 3.84 3.82 3.79 3.78 3.76
0.050 7.71 6.94 6.59 6.39 6.26 6.16 6.09 6.04 6 5.96 5.86 5.8 5.75 5.69 5.66 5.63
4 0.025 12.22 10.65 9.98 9.6 9.36 9.2 9.07 8.98 8.9 8.84 8.66 8.56 8.46 8.36 8.31 8.26
0.010 21.2 18 16.69 15.98 15.52 15.21 14.98 14.8 14.66 14.55 14.2 14.02 13.84 13.65 13.56 13.47
0.001 74.14 61.25 56.18 53.44 51.71 50.53 49.66 49 48.47 48.05 46.76 46.1 45.43 44.75 44.4 44.09
0.100 4.06 3.78 3.62 3.52 3.45 3.4 3.37 3.34 3.32 3.3 3.24 3.21 3.17 3.14 3.12 3.11
0.050 6.61 5.79 5.41 5.19 5.05 4.95 4.88 4.82 4.77 4.74 4.62 4.56 4.5 4.43 4.4 4.37
5 0.025 10.01 8.43 7.76 7.39 7.15 6.98 6.85 6.76 6.68 6.62 6.43 6.33 6.23 6.12 6.07 6.02
0.010 16.26 13.27 12.06 11.39 10.97 10.67 10.46 10.29 10.16 10.05 9.72 9.55 9.38 9.2 9.11 9.03
0.001 47.18 37.12 33.2 31.09 29.75 28.83 28.16 27.65 27.24 26.92 25.91 25.39 24.87 24.33 24.06 23.82
0.100 3.78 3.46 3.29 3.18 3.11 3.05 3.01 2.98 2.96 2.94 2.87 2.84 2.8 2.76 2.74 2.72
0.050 5.99 5.14 4.76 4.53 4.39 4.28 4.21 4.15 4.1 4.06 3.94 3.87 3.81 3.74 3.7 3.67
6 0.025 8.81 7.26 6.6 6.23 5.99 5.82 5.7 5.6 5.52 5.46 5.27 5.17 5.07 4.96 4.9 4.86
0.010 13.75 10.92 9.78 9.15 8.75 8.47 8.26 8.1 7.98 7.87 7.56 7.4 7.23 7.06 6.97 6.89
0.001 35.51 27 23.7 21.92 20.8 20.03 19.46 19.03 18.69 18.41 17.56 17.12 16.67 16.21 15.98 15.77
0.100 3.59 3.26 3.07 2.96 2.88 2.83 2.78 2.75 2.72 2.7 2.63 2.59 2.56 2.51 2.49 2.47
0.050 5.59 4.74 4.35 4.12 3.97 3.87 3.79 3.73 3.68 3.64 3.51 3.44 3.38 3.3 3.27 3.23
7 0.025 8.07 6.54 5.89 5.52 5.29 5.12 4.99 4.9 4.82 4.76 4.57 4.47 4.36 4.25 4.2 4.15
0.010 12.25 9.55 8.45 7.85 7.46 7.19 6.99 6.84 6.72 6.62 6.31 6.16 5.99 5.82 5.74 5.66
0.001 29.25 21.69 18.77 17.2 16.21 15.52 15.02 14.63 14.33 14.08 13.32 12.93 12.53 12.12 11.91 11.72
0.100 3.46 3.11 2.92 2.81 2.73 2.67 2.62 2.59 2.56 2.54 2.46 2.42 2.38 2.34 2.32 2.3
0.050 5.32 4.46 4.07 3.84 3.69 3.58 3.5 3.44 3.39 3.35 3.22 3.15 3.08 3.01 2.97 2.93
8 0.025 7.57 6.06 5.42 5.05 4.82 4.65 4.53 4.43 4.36 4.3 4.1 4 3.89 3.78 3.73 3.68
0.010 11.26 8.65 7.59 7.01 6.63 6.37 6.18 6.03 5.91 5.81 5.52 5.36 5.2 5.03 4.95 4.87
0.001 25.41 18.49 15.83 14.39 13.48 12.86 12.4 12.05 11.77 11.54 10.84 10.48 10.11 9.73 9.53 9.36
0.100 3.36 3.01 2.81 2.69 2.61 2.55 2.51 2.47 2.44 2.42 2.34 2.3 2.25 2.21 2.18 2.16
0.050 5.12 4.26 3.86 3.63 3.48 3.37 3.29 3.23 3.18 3.14 3.01 2.94 2.86 2.79 2.75 2.71
9 0.025 7.21 5.71 5.08 4.72 4.48 4.32 4.2 4.1 4.03 3.96 3.77 3.67 3.56 3.45 3.39 3.34
0.010 10.56 8.02 6.99 6.42 6.06 5.8 5.61 5.47 5.35 5.26 4.96 4.81 4.65 4.48 4.4 4.32
0.001 22.86 16.39 13.9 12.56 11.71 11.13 10.7 10.37 10.11 9.89 9.24 8.9 8.55 8.19 8 7.84
0.100 3.29 2.92 2.73 2.61 2.52 2.46 2.41 2.38 2.35 2.32 2.24 2.2 2.16 2.11 2.08 2.06
0.050 4.96 4.1 3.71 3.48 3.33 3.22 3.14 3.07 3.02 2.98 2.85 2.77 2.7 2.62 2.58 2.54
10 0.025 6.94 5.46 4.83 4.47 4.24 4.07 3.95 3.85 3.78 3.72 3.52 3.42 3.31 3.2 3.14 3.09
0.010 10.04 7.56 6.55 5.99 5.64 5.39 5.2 5.06 4.94 4.85 4.56 4.41 4.25 4.08 4 3.92
0.001 21.04 14.91 12.55 11.28 10.48 9.93 9.52 9.2 8.96 8.75 8.13 7.8 7.47 7.12 6.94 6.78
0.100 3.18 2.81 2.61 2.48 2.39 2.33 2.28 2.24 2.21 2.19 2.1 2.06 2.01 1.96 1.93 1.91
0.050 4.75 3.89 3.49 3.26 3.11 3 2.91 2.85 2.8 2.75 2.62 2.54 2.47 2.38 2.34 2.3
12 0.025 6.55 5.1 4.47 4.12 3.89 3.73 3.61 3.51 3.44 3.37 3.18 3.07 2.96 2.85 2.79 2.73
0.010 9.33 6.93 5.95 5.41 5.06 4.82 4.64 4.5 4.39 4.3 4.01 3.86 3.7 3.54 3.45 3.37
0.001 18.64 12.97 10.8 9.63 8.89 8.38 8 7.71 7.48 7.29 6.71 6.4 6.09 5.76 5.59 5.44
0.100 3.07 2.7 2.49 2.36 2.27 2.21 2.16 2.12 2.09 2.06 1.97 1.92 1.87 1.82 1.79 1.76
0.050 4.54 3.68 3.29 3.06 2.9 2.79 2.71 2.64 2.59 2.54 2.4 2.33 2.25 2.16 2.11 2.07
15 0.025 6.2 4.77 4.15 3.8 3.58 3.41 3.29 3.2 3.12 3.06 2.86 2.76 2.64 2.52 2.46 2.4
0.010 8.68 6.36 5.42 4.89 4.56 4.32 4.14 4 3.89 3.8 3.52 3.37 3.21 3.05 2.96 2.88
0.001 16.59 11.34 9.34 8.25 7.57 7.09 6.74 6.47 6.26 6.08 5.54 5.25 4.95 4.64 4.47 4.33
0.100 2.97 2.59 2.38 2.25 2.16 2.09 2.04 2 1.96 1.94 1.84 1.79 1.74 1.68 1.64 1.61
0.050 4.35 3.49 3.1 2.87 2.71 2.6 2.51 2.45 2.39 2.35 2.2 2.12 2.04 1.95 1.9 1.85
20 0.025 5.87 4.46 3.86 3.51 3.29 3.13 3.01 2.91 2.84 2.77 2.57 2.46 2.35 2.22 2.16 2.09
0.010 8.1 5.85 4.94 4.43 4.1 3.87 3.7 3.56 3.46 3.37 3.09 2.94 2.78 2.61 2.52 2.43
0.001 14.82 9.95 8.1 7.1 6.46 6.02 5.69 5.44 5.24 5.08 4.56 4.29 4 3.7 3.54 3.4
0.100 2.92 2.53 2.32 2.18 2.09 2.02 1.97 1.93 1.89 1.87 1.77 1.72 1.66 1.59 1.56 1.52
0.050 4.24 3.39 2.99 2.76 2.6 2.49 2.4 2.34 2.28 2.24 2.09 2.01 1.92 1.82 1.77 1.72
25 0.025 5.69 4.29 3.69 3.35 3.13 2.97 2.85 2.75 2.68 2.61 2.41 2.3 2.18 2.05 1.98 1.91
0.010 7.77 5.57 4.68 4.18 3.85 3.63 3.46 3.32 3.22 3.13 2.85 2.7 2.54 2.36 2.27 2.18
0.001 13.88 9.22 7.45 6.49 5.89 5.46 5.15 4.91 4.71 4.56 4.06 3.79 3.52 3.22 3.06 2.91
0.100 2.81 2.41 2.2 2.06 1.97 1.9 1.84 1.8 1.76 1.73 1.63 1.57 1.5 1.42 1.38 1.33
0.050 4.03 3.18 2.79 2.56 2.4 2.29 2.2 2.13 2.07 2.03 1.87 1.78 1.69 1.58 1.51 1.45
50 0.025 5.34 3.97 3.39 3.05 2.83 2.67 2.55 2.46 2.38 2.32 2.11 1.99 1.87 1.72 1.64 1.56
0.010 7.17 5.06 4.2 3.72 3.41 3.19 3.02 2.89 2.78 2.7 2.42 2.27 2.1 1.91 1.8 1.7
0.001 12.22 7.96 6.34 5.46 4.9 4.51 4.22 4 3.82 3.67 3.2 2.95 2.68 2.38 2.21 2.05
0.100 2.76 2.36 2.14 2 1.91 1.83 1.78 1.73 1.69 1.66 1.56 1.49 1.42 1.34 1.28 1.22
0.050 3.94 3.09 2.7 2.46 2.31 2.19 2.1 2.03 1.97 1.93 1.77 1.68 1.57 1.45 1.38 1.3
100 0.025 5.18 3.83 3.25 2.92 2.7 2.54 2.42 2.32 2.24 2.18 1.97 1.85 1.71 1.56 1.46 1.36
0.010 6.9 4.82 3.98 3.51 3.21 2.99 2.82 2.69 2.59 2.5 2.22 2.07 1.89 1.69 1.57 1.45
0.001 11.5 7.41 5.86 5.02 4.48 4.11 3.83 3.61 3.44 3.3 2.84 2.59 2.32 2.01 1.83 1.64
0.100 2.73 2.33 2.11 1.97 1.88 1.8 1.75 1.7 1.66 1.63 1.52 1.46 1.38 1.29 1.23 1.16
0.050 3.89 3.04 2.65 2.42 2.26 2.14 2.06 1.98 1.93 1.88 1.72 1.62 1.52 1.39 1.3 1.21
200 0.025 5.1 3.76 3.18 2.85 2.63 2.47 2.35 2.26 2.18 2.11 1.9 1.78 1.64 1.47 1.37 1.25
0.010 6.76 4.71 3.88 3.41 3.11 2.89 2.73 2.6 2.5 2.41 2.13 1.97 1.79 1.58 1.45 1.3
0.001 11.15 7.15 5.63 4.81 4.29 3.92 3.65 3.43 3.26 3.12 2.67 2.42 2.15 1.83 1.64 1.43
0.100 2.71 2.31 2.09 1.95 1.85 1.78 1.72 1.68 1.64 1.61 1.49 1.43 1.35 1.25 1.18 1.08
0.050 3.85 3 2.61 2.38 2.22 2.11 2.02 1.95 1.89 1.84 1.68 1.58 1.47 1.33 1.24 1.11
1000 0.025 5.04 3.7 3.13 2.8 2.58 2.42 2.3 2.2 2.13 2.06 1.96 1.85 1.64 1.45 1.41 1.29
0.010 6.66 4.63 3.8 3.34 3.04 2.82 2.66 2.53 2.43 2.34 2.06 1.9 1.72 1.5 1.35 1.16
0.001 10.89 6.96 5.46 4.65 4.14 3.78 3.51 3.3 3.13 2.99 2.54 2.3 2.02 1.69 1.49 1.22

Table E

X^2 p
df 0.25 0.2 0.15 0.1 0.05 0.025 0.02 0.01 0.005 0.0025 0.001 0.0005
1 1.32 1.64 2.07 2.71 3.84 5.02 5.41 6.63 7.88 9.14 10.83 12.12
2 2.77 3.22 3.79 4.61 5.99 7.38 7.82 9.21 10.60 11.98 13.82 15.20
3 4.11 4.64 5.32 6.25 7.81 9.35 9.84 11.34 12.84 14.32 16.27 17.73
4 5.39 5.99 6.74 7.78 9.49 11.14 11.67 13.28 14.86 16.42 18.47 20.00
5 6.63 7.29 8.12 9.24 11.07 12.83 13.39 15.09 16.75 18.39 20.51 22.11
6 7.84 8.56 9.45 10.64 12.59 14.45 15.03 16.81 18.55 20.25 22.46 24.10
7 9.04 9.80 10.75 12.02 14.07 16.01 16.62 18.48 20.28 22.04 24.32 26.02
8 10.22 11.03 12.03 13.36 15.51 17.53 18.17 20.09 21.95 23.77 26.12 27.87
9 11.39 12.24 13.29 14.68 16.92 19.02 19.68 21.67 23.59 25.46 27.88 29.67
10 12.55 13.44 14.53 15.99 18.31 20.48 21.16 23.21 25.19 27.11 29.59 31.42
11 13.70 14.63 15.77 17.28 19.68 21.92 22.62 24.72 26.76 28.73 31.26 33.14
12 14.85 15.81 16.99 18.55 21.03 23.34 24.05 26.22 28.30 30.32 32.91 34.82
13 15.98 16.98 18.20 19.81 22.36 24.74 25.47 27.69 29.82 31.88 34.53 36.48
14 17.12 18.15 19.41 21.06 23.68 26.12 26.87 29.14 31.32 33.43 36.12 38.11
15 18.25 19.31 20.60 22.31 25.00 27.49 28.26 30.58 32.80 34.95 37.70 39.72
16 19.37 20.47 21.79 23.54 26.30 28.85 29.63 32.00 34.27 36.46 39.25 41.31
17 20.49 21.61 22.98 24.77 27.59 30.19 31.00 33.41 35.72 37.95 40.79 42.88
18 21.60 22.76 24.16 25.99 28.87 31.53 32.35 34.81 37.16 39.42 42.31 44.43
19 22.72 23.90 25.33 27.20 30.14 32.85 33.69 36.19 38.58 40.88 43.82 45.97
20 23.83 25.04 26.50 28.41 31.41 34.17 35.02 37.57 40.00 42.34 45.31 47.50
21 24.93 26.17 27.66 29.62 32.67 35.48 36.34 38.93 41.40 43.78 46.80 49.01
22 26.04 27.30 28.82 30.81 33.92 36.78 37.66 40.29 42.80 45.20 48.27 50.51
23 27.14 28.43 29.98 32.01 35.17 38.08 38.97 41.64 44.18 46.62 49.73 52.00
24 28.24 29.55 31.13 33.20 36.42 39.36 40.27 42.98 45.56 48.03 51.18 53.48
25 29.34 30.68 32.28 34.38 37.65 40.65 41.57 44.31 46.93 49.44 52.62 54.95
26 30.43 31.79 33.43 35.56 38.89 41.92 42.86 45.64 48.29 50.83 54.05 56.41
27 31.53 32.91 34.57 36.74 40.11 43.19 44.14 46.96 49.64 52.22 55.48 57.86
28 32.62 34.03 35.71 37.92 41.34 44.46 45.42 48.28 50.99 53.59 56.89 59.30
29 33.71 35.14 36.85 39.09 42.56 45.72 46.69 49.59 52.34 54.97 58.30 60.73
30 34.80 36.25 37.99 40.26 43.77 46.98 47.96 50.89 53.67 56.33 59.70 62.16
40 45.62 47.27 49.24 51.81 55.76 59.34 60.44 63.69 66.77 69.70 73.40 76.09
50 56.33 58.16 60.35 63.17 67.50 71.42 72.61 76.15 79.49 82.66 86.66 89.56
60 66.98 68.97 71.34 74.40 79.08 83.30 84.58 88.38 91.95 95.34 99.61 102.70
80 88.13 90.41 93.11 96.58 101.90 106.60 108.10 112.30 116.30 120.10 124.80 128.30
100 109.10 111.70 114.70 118.50 124.30 129.60 131.10 135.80 140.20 144.30 149.40 153.20
Chi-square distribution critical values
Table entry for p is the
critical value X* with probability
p lying to its right

SPSS output for the one-way ANOVA

The ANOVA found is significant (F = 12.080)

F = 12.08 > 10.80

Thus p < 0.001

ANOVA

BPS 10.17 ANOVA F test
Do nematodes affect plant growth? A botanist prepares 16 identical planting pots and Seedling growth
adds different numbers of nematodes into the pots. Seedling growth is recorded 16 days later. 0 nematodes 10.8 9.1 13.5 9.2
1,000 nematodes 11.1 11.1 8.2 11.3
Seedling growth x bar i overall x bar s i 5,000 nematodes 5.4 4.6 7.4 5
0 10.8 9.1 13.5 9.2 10.65 8.03125 2.0534523775 10,000 nematodes 5.8 5.3 3.2 7.5
1,000 11.1 11.1 8.2 11.3 10.425 8.03125 1.486326568
5000 5.4 4.6 7.4 5 5.6 8.03125 1.2436505404
10000 5.8 5.3 3.2 7.5 5.45 8.03125 1.7710637105
ANOVA
Hypotheses all mu the same vs. not all mu the same Source of Variation SS df MS F P-value F crit
Conditions required s i max no more than s i min; distributions "roughly" normal Between Groups 100.646875 3 33.5489583333 12.0797389543 0.0006162913 3.4902996049
Within Groups 33.3275 12 2.7772916667
MSG = mean square for groups ( = SSG / df groups)
MSE = mean square for errors ( = SSE / df overall) Total 133.974375 15
F = MSG = variation among sample means
MSE variation among individuals in same sample
MSG = sum ( ni(x bar i - x bar)^2 )
I -1 Df = I -1
I is the number of samples/groups/conditions compared
MSE = sum ( (ni -1)si^2 ) N is the total number of measurments across all samples
N - I Df = N - I
Menu/Tools/DataAnalysis/AnovaSingleFactor
Anova: Single Factor
SUMMARY
Groups Count Sum Average Variance
0 nematode 4 42.6 10.65 4.2166666667
1000 nematodes 4 41.7 10.425 2.2091666667
5000 nematodes 4 22.4 5.6 1.5466666667
10000 nematodes 4 21.8 5.45 3.1366666667
ANOVA
Source of Variation SS df MS F P-value F crit
Between Groups 100.646875 3 33.5489583333 12.0797389543 0.0006162913 3.4902996049
Within Groups 33.3275 12 2.7772916667
Total 133.974375 15
&L&F - &A - &D&RBio7 s03 - Baldi Discussions

ANOVA

# of nematodes in pots
seedling growth

Review

Review of concepts Estimating the parameter mu Estimating the parameter p
(population mean, quatitative variable) (population proportion, categorical)
1 sample sigma known: sigma unknown:
variable x_bar x_bar p_hat
distibution z distribution t distribution z distribution
Ho value of mu under Ho value of mu under Ho value of p under Ho
spread sigma/sqrt(n) (s/sqrt(n)) sqrt(p(1-p)/n)
deg. freedom df = n - 1
2 samples, sigma known: sigma unknown:
paired variable x_bar for difference x_bar for difference
distibution z distribution t distribution Not applicable,
Ho mu difference = 0 mu difference = 0 Categorical data
spread sigma/sqrt(n) s/sqrt(n)
deg. freedom df = n - 1
2 samples, variable x_bar1 - x_bar2 p_hat1 - p_hat2
independt distribution t distribution z distribution
Ho mu1 - mu2 = 0 p1 - p2 = 0
spread sqrt( s1^2/n1 + s2^2/n2) sqrt[ p1(1-p1)/n1 + p2(1-p2)/n2 ]
deg. freedom df aprox. smallest(n1, n2) - 1 use p_hat1 and p_hat2 for estimating
>2 samples distribution F distribution - ANOVA Chi square distribution
( F = MSG / MSE ) ( X2 = sum (expect - actual)^2/expect )
Ho all mu are the same (alt. not all the same) all p are the same (alt. not all the same)
deg. freedom df numer = I - 1 (I: # samples compared) df = (r - 1)(c - 1)
df denom = N - I (N: # measurments)
SSG
SSE
MSG
MSE
Df numerator
Df denominator
F* corresponding to alpha 0.05 for the 2 degrees of freedom 3 and 12
SSG
Df numerator
MSG
F* corresponding to alpha 0.05 for the 2 degrees of freedom 3 and 12
SSE
Df denominator
MSE
&L&F - &A - &D&RBio7 s03 - Baldi Discussions

Exercises

What test do you need to perform? Final review
What are your hypotheses?
What are the characteristics of the distribution you will use?
* During an angiogram, heart problems can be examined using a catheter threaded into the heart from a vein in the patient's leg. The manufacturer
must maintain a diameter of 2 mm for the catheters (manufacturing stdev 0.1 mm). Random samples of 5 catheters are taken and measured daily.
quantitative; sigma known; 1 sample z test (no df) Ho: mu = 2 mm; Ha: mu ≠ 2 mm (2 sided)
* A researcher wants to see if there is a significant difference in Resting Pulse Rates for men and women.
For the study, 28 men and 24 women had their pulse rate measured at rest in the lab.
quantitative; indept samples; 2 sample t test, df 23 Ho: mu men - mu women = 0 (vs. ≠0, 2 sided)
* The Journal of Applied Psychology reported in 2002 a study testing whether the content of TV shows influences the ability of
viewers to recall brands presented during commercials. Volunteers were randomly assigned to watch 1 of 3 programs, each
containing the same 9 commercials. One program had violent content, another sexual content, and the other one neutral content.
After the shows, subjects (20 in each group) were tested to see how many brands they could recall.
quantitative; anova, dfn 2 dfd 57 (N = 60, I = 3) Ho: mu violent = mu sexual = mu neutral
* Does ginkgo biloba enhance memory? Subjects were assigned randomly to take either ginkgo biloba suplements (35 subjects)
or a placebo (30 subjects). Their memory was later tested to see if it had improved.
quantitative; indept samples; 2 sample t test, df 29 Ho: mu improv GB - mu improv Pl = 0 (vs. >0, 1 sided)
* Common folk wisdom has it that drinking cranberry juice helps prevent urinary track infection in women. A study published in the
British Medical Journal in 2001 tested women assigned randomly to 1 of 3 groups: daily intake of cranberry juice, daily intake
of lactobacillus drink, or neither of these 2 drinks. The proportion of women developing at least 1 urinary trackt infection during the
6 month study was recorded for each group (50 women in each group).
categorical; chi square, df 2 (3 by 2 table) Ho: p cranb. = p lactob. = p neither
* Cuckoos lay their eggs in the nests of other bird species which will take on the burden of parenting the parasite offspring.
Cuckoos parasite several bird species, all laying eggs of different sizes. Do cuckoos change the size of their eggs to mimic that
of the bird species in which the eggs are laid? Cuckoo egg length were measured for eggs that had been laid in nests of either
sparrow (14 eggs), robin (16 eggs) or wagtail (15 eggs) host bird species.
quantitative; anova, dfn 2 dfd 42 (N = 45, I = 3) Ho: mu sparrow = mu robin = mu wagtail
* Many dairy cows now receive injections of BST, a hormone intended to spur greater milk production. The milk production of 60
Ayrshire dairy cows was recorded before and after they received a first injection of BST.
quantitative; match pair design; paired t-test, df 59 Ho: mu difference = 0 (vs. >0, 1 sided)
* Medical researchers followed 6272 Swedish men for 30 years to see if there was any association between the amount of fish
in their diet (never/selcom, small, moderate, large part of the diet) and prostate cancer.
categorical; chi square, df 3 (4 by 2 table) Ho: p seldom = p small = p moderate = p large
&L&F - &A - &D&RBio7 s03 - Baldi Discussions

Table A

z 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09
-3.4 0.0003 0.0003 0.0003 0.0003 0.0003 0.0003 0.0003 0.0003 0.0003 0.0002
-3.3 0.0005 0.0005 0.0005 0.0004 0.0004 0.0004 0.0004 0.0004 0.0004 0.0003
-3.2 0.0007 0.0007 0.0006 0.0006 0.0006 0.0006 0.0006 0.0005 0.0005 0.0005
-3.1 0.0010 0.0009 0.0009 0.0009 0.0008 0.0008 0.0008 0.0008 0.0007 0.0007
-3 0.0013 0.0013 0.0013 0.0012 0.0012 0.0011 0.0011 0.0011 0.0010 0.0010
-2.9 0.0019 0.0018 0.0018 0.0017 0.0016 0.0016 0.0015 0.0015 0.0014 0.0014 Table of areas under the
-2.8 0.0026 0.0025 0.0024 0.0023 0.0023 0.0022 0.0021 0.0021 0.0020 0.0019 Normal distribution curve
-2.7 0.0035 0.0034 0.0033 0.0032 0.0031 0.0030 0.0029 0.0028 0.0027 0.0026 to the left of z values
-2.6 0.0047 0.0045 0.0044 0.0043 0.0041 0.0040 0.0039 0.0038 0.0037 0.0036
-2.5 0.0062 0.0060 0.0059 0.0057 0.0055 0.0054 0.0052 0.0051 0.0049 0.0048
-2.4 0.0082 0.0080 0.0078 0.0075 0.0073 0.0071 0.0069 0.0068 0.0066 0.0064
-2.3 0.0107 0.0104 0.0102 0.0099 0.0096 0.0094 0.0091 0.0089 0.0087 0.0084
-2.2 0.0139 0.0136 0.0132 0.0129 0.0125 0.0122 0.0119 0.0116 0.0113 0.0110
-2.1 0.0179 0.0174 0.0170 0.0166 0.0162 0.0158 0.0154 0.0150 0.0146 0.0143
-2 0.0228 0.0222 0.0217 0.0212 0.0207 0.0202 0.0197 0.0192 0.0188 0.0183
-1.9 0.0287 0.0281 0.0274 0.0268 0.0262 0.0256 0.0250 0.0244 0.0239 0.0233
-1.8 0.0359 0.0351 0.0344 0.0336 0.0329 0.0322 0.0314 0.0307 0.0301 0.0294
-1.7 0.0446 0.0436 0.0427 0.0418 0.0409 0.0401 0.0392 0.0384 0.0375 0.0367
-1.6 0.0548 0.0537 0.0526 0.0516 0.0505 0.0495 0.0485 0.0475 0.0465 0.0455
-1.5 0.0668 0.0655 0.0643 0.0630 0.0618 0.0606 0.0594 0.0582 0.0571 0.0559
-1.4 0.0808 0.0793 0.0778 0.0764 0.0749 0.0735 0.0721 0.0708 0.0694 0.0681
-1.3 0.0968 0.0951 0.0934 0.0918 0.0901 0.0885 0.0869 0.0853 0.0838 0.0823
-1.2 0.1151 0.1131 0.1112 0.1093 0.1075 0.1056 0.1038 0.1020 0.1003 0.0985
-1.1 0.1357 0.1335 0.1314 0.1292 0.1271 0.1251 0.1230 0.1210 0.1190 0.1170
-1 0.1587 0.1562 0.1539 0.1515 0.1492 0.1469 0.1446 0.1423 0.1401 0.1379
-0.9 0.1841 0.1814 0.1788 0.1762 0.1736 0.1711 0.1685 0.1660 0.1635 0.1611
-0.8 0.2119 0.2090 0.2061 0.2033 0.2005 0.1977 0.1949 0.1922 0.1894 0.1867
-0.7 0.2420 0.2389 0.2358 0.2327 0.2296 0.2266 0.2236 0.2206 0.2177 0.2148
-0.6 0.2743 0.2709 0.2676 0.2643 0.2611 0.2578 0.2546 0.2514 0.2483 0.2451
-0.5 0.3085 0.3050 0.3015 0.2981 0.2946 0.2912 0.2877 0.2843 0.2810 0.2776
-0.4 0.3446 0.3409 0.3372 0.3336 0.3300 0.3264 0.3228 0.3192 0.3156 0.3121
-0.3 0.3821 0.3783 0.3745 0.3707 0.3669 0.3632 0.3594 0.3557 0.3520 0.3483
-0.2 0.4207 0.4168 0.4129 0.4090 0.4052 0.4013 0.3974 0.3936 0.3897 0.3859
-0.1 0.4602 0.4562 0.4522 0.4483 0.4443 0.4404 0.4364 0.4325 0.4286 0.4247
-0.0 0.5000 0.4960 0.4920 0.4880 0.4840 0.4801 0.4761 0.4721 0.4681 0.4641
0.0 0.5000 0.5040 0.5080 0.5120 0.5160 0.5199 0.5239 0.5279 0.5319 0.5359
0.1 0.5398 0.5438 0.5478 0.5517 0.5557 0.5596 0.5636 0.5675 0.5714 0.5753
0.2 0.5793 0.5832 0.5871 0.5910 0.5948 0.5987 0.6026 0.6064 0.6103 0.6141
0.3 0.6179 0.6217 0.6255 0.6293 0.6331 0.6368 0.6406 0.6443 0.6480 0.6517
0.4 0.6554 0.6591 0.6628 0.6664 0.6700 0.6736 0.6772 0.6808 0.6844 0.6879
0.5 0.6915 0.6950 0.6985 0.7019 0.7054 0.7088 0.7123 0.7157 0.7190 0.7224
0.6 0.7257 0.7291 0.7324 0.7357 0.7389 0.7422 0.7454 0.7486 0.7517 0.7549
0.7 0.7580 0.7611 0.7642 0.7673 0.7704 0.7734 0.7764 0.7794 0.7823 0.7852
0.8 0.7881 0.7910 0.7939 0.7967 0.7995 0.8023 0.8051 0.8078 0.8106 0.8133
0.9 0.8159 0.8186 0.8212 0.8238 0.8264 0.8289 0.8315 0.8340 0.8365 0.8389
1.0 0.8413 0.8438 0.8461 0.8485 0.8508 0.8531 0.8554 0.8577 0.8599 0.8621
1.1 0.8643 0.8665 0.8686 0.8708 0.8729 0.8749 0.8770 0.8790 0.8810 0.8830
1.2 0.8849 0.8869 0.8888 0.8907 0.8925 0.8944 0.8962 0.8980 0.8997 0.9015
1.3 0.9032 0.9049 0.9066 0.9082 0.9099 0.9115 0.9131 0.9147 0.9162 0.9177
1.4 0.9192 0.9207 0.9222 0.9236 0.9251 0.9265 0.9279 0.9292 0.9306 0.9319
1.5 0.9332 0.9345 0.9357 0.9370 0.9382 0.9394 0.9406 0.9418 0.9429 0.9441
1.6 0.9452 0.9463 0.9474 0.9484 0.9495 0.9505 0.9515 0.9525 0.9535 0.9545
1.7 0.9554 0.9564 0.9573 0.9582 0.9591 0.9599 0.9608 0.9616 0.9625 0.9633
1.8 0.9641 0.9649 0.9656 0.9664 0.9671 0.9678 0.9686 0.9693 0.9699 0.9706
1.9 0.9713 0.9719 0.9726 0.9732 0.9738 0.9744 0.9750 0.9756 0.9761 0.9767
2.0 0.9772 0.9778 0.9783 0.9788 0.9793 0.9798 0.9803 0.9808 0.9812 0.9817
2.1 0.9821 0.9826 0.9830 0.9834 0.9838 0.9842 0.9846 0.9850 0.9854 0.9857
2.2 0.9861 0.9864 0.9868 0.9871 0.9875 0.9878 0.9881 0.9884 0.9887 0.9890
2.3 0.9893 0.9896 0.9898 0.9901 0.9904 0.9906 0.9909 0.9911 0.9913 0.9916
2.4 0.9918 0.9920 0.9922 0.9925 0.9927 0.9929 0.9931 0.9932 0.9934 0.9936
2.5 0.9938 0.9940 0.9941 0.9943 0.9945 0.9946 0.9948 0.9949 0.9951 0.9952
2.6 0.9953 0.9955 0.9956 0.9957 0.9959 0.9960 0.9961 0.9962 0.9963 0.9964
2.7 0.9965 0.9966 0.9967 0.9968 0.9969 0.9970 0.9971 0.9972 0.9973 0.9974
2.8 0.9974 0.9975 0.9976 0.9977 0.9977 0.9978 0.9979 0.9979 0.9980 0.9981
2.9 0.9981 0.9982 0.9982 0.9983 0.9984 0.9984 0.9985 0.9985 0.9986 0.9986
3.0 0.9987 0.9987 0.9987 0.9988 0.9988 0.9989 0.9989 0.9989 0.9990 0.9990
3.1 0.9990 0.9991 0.9991 0.9991 0.9992 0.9992 0.9992 0.9992 0.9993 0.9993
3.2 0.9993 0.9993 0.9994 0.9994 0.9994 0.9994 0.9994 0.9995 0.9995 0.9995
3.3 0.9995 0.9995 0.9995 0.9996 0.9996 0.9996 0.9996 0.9996 0.9996 0.9997
3.4 0.9997 0.9997 0.9997 0.9997 0.9997 0.9997 0.9997 0.9997 0.9997 0.9998

Table C

t* Upper tail probability P
df 0.25 0.2 0.15 0.1 0.05 0.025 0.02 0.01 0.005 0.0025 0.001 0.0005
1 1.000 1.376 1.963 3.078 6.314 12.710 15.890 31.820 63.660 127.300 318.300 636.600
2 0.816 1.061 1.386 1.886 2.920 4.303 4.849 6.965 9.925 14.090 22.330 31.600
3 0.765 0.978 1.250 1.638 2.353 3.182 3.482 4.541 5.841 7.453 10.210 12.920
4 0.741 0.941 1.190 1.533 2.132 2.776 2.999 3.747 4.604 5.598 7.173 8.610
5 0.727 0.920 1.156 1.476 2.015 2.571 2.757 3.365 4.032 4.773 5.893 6.869
6 0.718 0.906 1.134 1.440 1.943 2.447 2.612 3.143 3.707 4.317 5.208 5.959
7 0.711 0.896 1.119 1.415 1.895 2.365 2.517 2.998 3.499 4.029 4.785 5.408
8 0.706 0.889 1.108 1.397 1.860 2.306 2.449 2.896 3.355 3.833 4.501 5.041
9 0.703 0.883 1.100 1.383 1.833 2.262 2.398 2.821 3.250 3.690 4.297 4.781
10 0.700 0.879 1.093 1.372 1.812 2.228 2.359 2.764 3.169 3.581 4.144 4.587
11 0.697 0.876 1.088 1.363 1.796 2.201 2.328 2.718 3.106 3.497 4.025 4.437
12 0.695 0.873 1.083 1.356 1.782 2.179 2.303 2.681 3.055 3.428 3.930 4.318
13 0.694 0.870 1.079 1.350 1.771 2.160 2.282 2.650 3.012 3.372 3.852 4.221
14 0.692 0.868 1.076 1.345 1.761 2.145 2.264 2.624 2.977 3.326 3.787 4.140
15 0.691 0.866 1.074 1.341 1.753 2.131 2.249 2.602 2.947 3.286 3.733 4.073
16 0.690 0.865 1.071 1.337 1.746 2.120 2.235 2.583 2.921 3.252 3.686 4.015
17 0.689 0.863 1.069 1.333 1.740 2.110 2.224 2.567 2.898 3.222 3.646 3.965
18 0.688 0.862 1.067 1.330 1.734 2.101 2.214 2.552 2.878 3.197 3.611 3.922
19 0.688 0.861 1.066 1.328 1.729 2.093 2.205 2.539 2.861 3.174 3.579 3.883
20 0.687 0.860 1.064 1.325 1.725 2.086 2.197 2.528 2.845 3.153 3.552 3.850
22 0.686 0.858 1.061 1.321 1.717 2.074 2.183 2.508 2.819 3.119 3.505 3.792
25 0.684 0.856 1.058 1.316 1.708 2.060 2.167 2.485 2.787 3.078 3.450 3.725
30 0.683 0.854 1.055 1.310 1.697 2.042 2.147 2.457 2.750 3.030 3.385 3.646
40 0.681 0.851 1.050 1.303 1.684 2.021 2.123 2.423 2.704 2.971 3.307 3.551
50 0.679 0.849 1.047 1.299 1.676 2.009 2.109 2.403 2.678 2.937 3.261 3.496
60 0.679 0.848 1.045 1.296 1.671 2.000 2.099 2.390 2.660 2.915 3.232 3.460
80 0.678 0.846 1.043 1.292 1.664 1.990 2.088 2.374 2.639 2.887 3.195 3.416
100 0.677 0.845 1.042 1.290 1.660 1.984 2.081 2.364 2.626 2.871 3.174 3.390
1,000 0.675 0.842 1.037 1.282 1.646 1.962 2.056 2.330 2.581 2.813 3.098 3.300
z* 0.674 0.841 1.036 1.282 1.645 1.960 2.054 2.326 2.576 2.807 3.091 3.291
50% 60% 70% 80% 90% 95% 96% 98% 99% 99.50% 99.80% 99.90%
Confidence level C

Table D

F* Degrees of freedom (Df) in the numerator
Proba "p" 1 2 3 4 5 6 7 8 9 10 15 20 30 60 120 1000
Df den. 0.100 39.86 49.5 53.59 55.83 57.24 58.2 58.91 59.44 59.86 60.19 61.22 61.74 62.26 62.79 63.06 63.3
0.050 161.45 199.5 215.71 224.58 230.16 233.99 236.77 238.88 240.54 241.88 245.95 248.01 250.1 252.2 253.25 254.19
1 0.025 647.79 799.5 864.16 899.58 921.85 937.11 948.22 956.66 963.28 968.63 984.87 993.1 1001.4 1009.8 1014 1017.7
0.010 4052.2 4999.5 5403.4 5624.6 5763.6 5859 5928.4 5981.1 6022.5 6055.8 6157.3 6208.7 6260.6 6313 6339.4 6362.7
0.001 405284 500000 540379 562500 576405 585937 592873 598144 602284 605621 615764 620908 626099 631337 633972 636301
0.100 8.53 9 9.16 9.24 9.29 9.33 9.35 9.37 9.38 9.39 9.42 9.44 9.46 9.47 9.48 9.49
0.050 18.51 19 19.16 19.25 19.3 19.33 19.35 19.37 19.38 19.4 19.43 19.45 19.46 19.48 19.49 19.49
2 0.025 38.51 39 39.17 39.25 39.3 39.33 39.36 39.37 39.39 39.4 39.43 39.45 39.46 39.48 39.49 39.5
0.010 98.5 99 99.17 99.25 99.3 99.33 99.36 99.37 99.39 99.4 99.43 99.45 99.47 99.48 99.49 99.5
0.001 998.5 999 999.17 999.25 999.3 999.33 999.36 999.37 999.39 999.4 999.43 999.45 999.47 999.48 999.49 999.5
0.100 5.54 5.46 5.39 5.34 5.31 5.28 5.27 5.25 5.24 5.23 5.2 5.18 5.17 5.15 5.14 5.13
0.050 10.13 9.55 9.28 9.12 9.01 8.94 8.89 8.85 8.81 8.79 8.7 8.66 8.62 8.57 8.55 8.53
3 0.025 17.44 16.04 15.44 15.1 14.88 14.73 14.62 14.54 14.47 14.42 14.25 14.17 14.08 13.99 13.95 13.91
0.010 34.12 30.82 29.46 28.71 28.24 27.91 27.67 27.49 27.35 27.23 26.87 26.69 26.5 26.32 26.22 26.14
0.001 167.03 148.5 141.11 137.1 134.58 132.85 131.58 130.62 129.86 129.25 127.37 126.42 125.45 124.47 123.97 123.53
0.100 4.54 4.32 4.19 4.11 4.05 4.01 3.98 3.95 3.94 3.92 3.87 3.84 3.82 3.79 3.78 3.76
0.050 7.71 6.94 6.59 6.39 6.26 6.16 6.09 6.04 6 5.96 5.86 5.8 5.75 5.69 5.66 5.63
4 0.025 12.22 10.65 9.98 9.6 9.36 9.2 9.07 8.98 8.9 8.84 8.66 8.56 8.46 8.36 8.31 8.26
0.010 21.2 18 16.69 15.98 15.52 15.21 14.98 14.8 14.66 14.55 14.2 14.02 13.84 13.65 13.56 13.47
0.001 74.14 61.25 56.18 53.44 51.71 50.53 49.66 49 48.47 48.05 46.76 46.1 45.43 44.75 44.4 44.09
0.100 4.06 3.78 3.62 3.52 3.45 3.4 3.37 3.34 3.32 3.3 3.24 3.21 3.17 3.14 3.12 3.11
0.050 6.61 5.79 5.41 5.19 5.05 4.95 4.88 4.82 4.77 4.74 4.62 4.56 4.5 4.43 4.4 4.37
5 0.025 10.01 8.43 7.76 7.39 7.15 6.98 6.85 6.76 6.68 6.62 6.43 6.33 6.23 6.12 6.07 6.02
0.010 16.26 13.27 12.06 11.39 10.97 10.67 10.46 10.29 10.16 10.05 9.72 9.55 9.38 9.2 9.11 9.03
0.001 47.18 37.12 33.2 31.09 29.75 28.83 28.16 27.65 27.24 26.92 25.91 25.39 24.87 24.33 24.06 23.82
0.100 3.78 3.46 3.29 3.18 3.11 3.05 3.01 2.98 2.96 2.94 2.87 2.84 2.8 2.76 2.74 2.72
0.050 5.99 5.14 4.76 4.53 4.39 4.28 4.21 4.15 4.1 4.06 3.94 3.87 3.81 3.74 3.7 3.67
6 0.025 8.81 7.26 6.6 6.23 5.99 5.82 5.7 5.6 5.52 5.46 5.27 5.17 5.07 4.96 4.9 4.86
0.010 13.75 10.92 9.78 9.15 8.75 8.47 8.26 8.1 7.98 7.87 7.56 7.4 7.23 7.06 6.97 6.89
0.001 35.51 27 23.7 21.92 20.8 20.03 19.46 19.03 18.69 18.41 17.56 17.12 16.67 16.21 15.98 15.77
0.100 3.59 3.26 3.07 2.96 2.88 2.83 2.78 2.75 2.72 2.7 2.63 2.59 2.56 2.51 2.49 2.47
0.050 5.59 4.74 4.35 4.12 3.97 3.87 3.79 3.73 3.68 3.64 3.51 3.44 3.38 3.3 3.27 3.23
7 0.025 8.07 6.54 5.89 5.52 5.29 5.12 4.99 4.9 4.82 4.76 4.57 4.47 4.36 4.25 4.2 4.15
0.010 12.25 9.55 8.45 7.85 7.46 7.19 6.99 6.84 6.72 6.62 6.31 6.16 5.99 5.82 5.74 5.66
0.001 29.25 21.69 18.77 17.2 16.21 15.52 15.02 14.63 14.33 14.08 13.32 12.93 12.53 12.12 11.91 11.72
0.100 3.46 3.11 2.92 2.81 2.73 2.67 2.62 2.59 2.56 2.54 2.46 2.42 2.38 2.34 2.32 2.3
0.050 5.32 4.46 4.07 3.84 3.69 3.58 3.5 3.44 3.39 3.35 3.22 3.15 3.08 3.01 2.97 2.93
8 0.025 7.57 6.06 5.42 5.05 4.82 4.65 4.53 4.43 4.36 4.3 4.1 4 3.89 3.78 3.73 3.68
0.010 11.26 8.65 7.59 7.01 6.63 6.37 6.18 6.03 5.91 5.81 5.52 5.36 5.2 5.03 4.95 4.87
0.001 25.41 18.49 15.83 14.39 13.48 12.86 12.4 12.05 11.77 11.54 10.84 10.48 10.11 9.73 9.53 9.36
0.100 3.36 3.01 2.81 2.69 2.61 2.55 2.51 2.47 2.44 2.42 2.34 2.3 2.25 2.21 2.18 2.16
0.050 5.12 4.26 3.86 3.63 3.48 3.37 3.29 3.23 3.18 3.14 3.01 2.94 2.86 2.79 2.75 2.71
9 0.025 7.21 5.71 5.08 4.72 4.48 4.32 4.2 4.1 4.03 3.96 3.77 3.67 3.56 3.45 3.39 3.34
0.010 10.56 8.02 6.99 6.42 6.06 5.8 5.61 5.47 5.35 5.26 4.96 4.81 4.65 4.48 4.4 4.32
0.001 22.86 16.39 13.9 12.56 11.71 11.13 10.7 10.37 10.11 9.89 9.24 8.9 8.55 8.19 8 7.84
0.100 3.29 2.92 2.73 2.61 2.52 2.46 2.41 2.38 2.35 2.32 2.24 2.2 2.16 2.11 2.08 2.06
0.050 4.96 4.1 3.71 3.48 3.33 3.22 3.14 3.07 3.02 2.98 2.85 2.77 2.7 2.62 2.58 2.54
10 0.025 6.94 5.46 4.83 4.47 4.24 4.07 3.95 3.85 3.78 3.72 3.52 3.42 3.31 3.2 3.14 3.09
0.010 10.04 7.56 6.55 5.99 5.64 5.39 5.2 5.06 4.94 4.85 4.56 4.41 4.25 4.08 4 3.92
0.001 21.04 14.91 12.55 11.28 10.48 9.93 9.52 9.2 8.96 8.75 8.13 7.8 7.47 7.12 6.94 6.78
0.100 3.18 2.81 2.61 2.48 2.39 2.33 2.28 2.24 2.21 2.19 2.1 2.06 2.01 1.96 1.93 1.91
0.050 4.75 3.89 3.49 3.26 3.11 3 2.91 2.85 2.8 2.75 2.62 2.54 2.47 2.38 2.34 2.3
12 0.025 6.55 5.1 4.47 4.12 3.89 3.73 3.61 3.51 3.44 3.37 3.18 3.07 2.96 2.85 2.79 2.73
0.010 9.33 6.93 5.95 5.41 5.06 4.82 4.64 4.5 4.39 4.3 4.01 3.86 3.7 3.54 3.45 3.37
0.001 18.64 12.97 10.8 9.63 8.89 8.38 8 7.71 7.48 7.29 6.71 6.4 6.09 5.76 5.59 5.44
0.100 3.07 2.7 2.49 2.36 2.27 2.21 2.16 2.12 2.09 2.06 1.97 1.92 1.87 1.82 1.79 1.76
0.050 4.54 3.68 3.29 3.06 2.9 2.79 2.71 2.64 2.59 2.54 2.4 2.33 2.25 2.16 2.11 2.07
15 0.025 6.2 4.77 4.15 3.8 3.58 3.41 3.29 3.2 3.12 3.06 2.86 2.76 2.64 2.52 2.46 2.4
0.010 8.68 6.36 5.42 4.89 4.56 4.32 4.14 4 3.89 3.8 3.52 3.37 3.21 3.05 2.96 2.88
0.001 16.59 11.34 9.34 8.25 7.57 7.09 6.74 6.47 6.26 6.08 5.54 5.25 4.95 4.64 4.47 4.33
0.100 2.97 2.59 2.38 2.25 2.16 2.09 2.04 2 1.96 1.94 1.84 1.79 1.74 1.68 1.64 1.61
0.050 4.35 3.49 3.1 2.87 2.71 2.6 2.51 2.45 2.39 2.35 2.2 2.12 2.04 1.95 1.9 1.85
20 0.025 5.87 4.46 3.86 3.51 3.29 3.13 3.01 2.91 2.84 2.77 2.57 2.46 2.35 2.22 2.16 2.09
0.010 8.1 5.85 4.94 4.43 4.1 3.87 3.7 3.56 3.46 3.37 3.09 2.94 2.78 2.61 2.52 2.43
0.001 14.82 9.95 8.1 7.1 6.46 6.02 5.69 5.44 5.24 5.08 4.56 4.29 4 3.7 3.54 3.4
0.100 2.92 2.53 2.32 2.18 2.09 2.02 1.97 1.93 1.89 1.87 1.77 1.72 1.66 1.59 1.56 1.52
0.050 4.24 3.39 2.99 2.76 2.6 2.49 2.4 2.34 2.28 2.24 2.09 2.01 1.92 1.82 1.77 1.72
25 0.025 5.69 4.29 3.69 3.35 3.13 2.97 2.85 2.75 2.68 2.61 2.41 2.3 2.18 2.05 1.98 1.91
0.010 7.77 5.57 4.68 4.18 3.85 3.63 3.46 3.32 3.22 3.13 2.85 2.7 2.54 2.36 2.27 2.18
0.001 13.88 9.22 7.45 6.49 5.89 5.46 5.15 4.91 4.71 4.56 4.06 3.79 3.52 3.22 3.06 2.91
0.100 2.81 2.41 2.2 2.06 1.97 1.9 1.84 1.8 1.76 1.73 1.63 1.57 1.5 1.42 1.38 1.33
0.050 4.03 3.18 2.79 2.56 2.4 2.29 2.2 2.13 2.07 2.03 1.87 1.78 1.69 1.58 1.51 1.45
50 0.025 5.34 3.97 3.39 3.05 2.83 2.67 2.55 2.46 2.38 2.32 2.11 1.99 1.87 1.72 1.64 1.56
0.010 7.17 5.06 4.2 3.72 3.41 3.19 3.02 2.89 2.78 2.7 2.42 2.27 2.1 1.91 1.8 1.7
0.001 12.22 7.96 6.34 5.46 4.9 4.51 4.22 4 3.82 3.67 3.2 2.95 2.68 2.38 2.21 2.05
0.100 2.76 2.36 2.14 2 1.91 1.83 1.78 1.73 1.69 1.66 1.56 1.49 1.42 1.34 1.28 1.22
0.050 3.94 3.09 2.7 2.46 2.31 2.19 2.1 2.03 1.97 1.93 1.77 1.68 1.57 1.45 1.38 1.3
100 0.025 5.18 3.83 3.25 2.92 2.7 2.54 2.42 2.32 2.24 2.18 1.97 1.85 1.71 1.56 1.46 1.36
0.010 6.9 4.82 3.98 3.51 3.21 2.99 2.82 2.69 2.59 2.5 2.22 2.07 1.89 1.69 1.57 1.45
0.001 11.5 7.41 5.86 5.02 4.48 4.11 3.83 3.61 3.44 3.3 2.84 2.59 2.32 2.01 1.83 1.64
0.100 2.73 2.33 2.11 1.97 1.88 1.8 1.75 1.7 1.66 1.63 1.52 1.46 1.38 1.29 1.23 1.16
0.050 3.89 3.04 2.65 2.42 2.26 2.14 2.06 1.98 1.93 1.88 1.72 1.62 1.52 1.39 1.3 1.21
200 0.025 5.1 3.76 3.18 2.85 2.63 2.47 2.35 2.26 2.18 2.11 1.9 1.78 1.64 1.47 1.37 1.25
0.010 6.76 4.71 3.88 3.41 3.11 2.89 2.73 2.6 2.5 2.41 2.13 1.97 1.79 1.58 1.45 1.3
0.001 11.15 7.15 5.63 4.81 4.29 3.92 3.65 3.43 3.26 3.12 2.67 2.42 2.15 1.83 1.64 1.43
0.100 2.71 2.31 2.09 1.95 1.85 1.78 1.72 1.68 1.64 1.61 1.49 1.43 1.35 1.25 1.18 1.08
0.050 3.85 3 2.61 2.38 2.22 2.11 2.02 1.95 1.89 1.84 1.68 1.58 1.47 1.33 1.24 1.11
1000 0.025 5.04 3.7 3.13 2.8 2.58 2.42 2.3 2.2 2.13 2.06 1.96 1.85 1.64 1.45 1.41 1.29
0.010 6.66 4.63 3.8 3.34 3.04 2.82 2.66 2.53 2.43 2.34 2.06 1.9 1.72 1.5 1.35 1.16
0.001 10.89 6.96 5.46 4.65 4.14 3.78 3.51 3.3 3.13 2.99 2.54 2.3 2.02 1.69 1.49 1.22

Table E

X^2 p
df 0.25 0.2 0.15 0.1 0.05 0.025 0.02 0.01 0.005 0.0025 0.001 0.0005
1 1.32 1.64 2.07 2.71 3.84 5.02 5.41 6.63 7.88 9.14 10.83 12.12
2 2.77 3.22 3.79 4.61 5.99 7.38 7.82 9.21 10.60 11.98 13.82 15.20
3 4.11 4.64 5.32 6.25 7.81 9.35 9.84 11.34 12.84 14.32 16.27 17.73
4 5.39 5.99 6.74 7.78 9.49 11.14 11.67 13.28 14.86 16.42 18.47 20.00
5 6.63 7.29 8.12 9.24 11.07 12.83 13.39 15.09 16.75 18.39 20.51 22.11
6 7.84 8.56 9.45 10.64 12.59 14.45 15.03 16.81 18.55 20.25 22.46 24.10
7 9.04 9.80 10.75 12.02 14.07 16.01 16.62 18.48 20.28 22.04 24.32 26.02
8 10.22 11.03 12.03 13.36 15.51 17.53 18.17 20.09 21.95 23.77 26.12 27.87
9 11.39 12.24 13.29 14.68 16.92 19.02 19.68 21.67 23.59 25.46 27.88 29.67
10 12.55 13.44 14.53 15.99 18.31 20.48 21.16 23.21 25.19 27.11 29.59 31.42
11 13.70 14.63 15.77 17.28 19.68 21.92 22.62 24.72 26.76 28.73 31.26 33.14
12 14.85 15.81 16.99 18.55 21.03 23.34 24.05 26.22 28.30 30.32 32.91 34.82
13 15.98 16.98 18.20 19.81 22.36 24.74 25.47 27.69 29.82 31.88 34.53 36.48
14 17.12 18.15 19.41 21.06 23.68 26.12 26.87 29.14 31.32 33.43 36.12 38.11
15 18.25 19.31 20.60 22.31 25.00 27.49 28.26 30.58 32.80 34.95 37.70 39.72
16 19.37 20.47 21.79 23.54 26.30 28.85 29.63 32.00 34.27 36.46 39.25 41.31
17 20.49 21.61 22.98 24.77 27.59 30.19 31.00 33.41 35.72 37.95 40.79 42.88
18 21.60 22.76 24.16 25.99 28.87 31.53 32.35 34.81 37.16 39.42 42.31 44.43
19 22.72 23.90 25.33 27.20 30.14 32.85 33.69 36.19 38.58 40.88 43.82 45.97
20 23.83 25.04 26.50 28.41 31.41 34.17 35.02 37.57 40.00 42.34 45.31 47.50
21 24.93 26.17 27.66 29.62 32.67 35.48 36.34 38.93 41.40 43.78 46.80 49.01
22 26.04 27.30 28.82 30.81 33.92 36.78 37.66 40.29 42.80 45.20 48.27 50.51
23 27.14 28.43 29.98 32.01 35.17 38.08 38.97 41.64 44.18 46.62 49.73 52.00
24 28.24 29.55 31.13 33.20 36.42 39.36 40.27 42.98 45.56 48.03 51.18 53.48
25 29.34 30.68 32.28 34.38 37.65 40.65 41.57 44.31 46.93 49.44 52.62 54.95
26 30.43 31.79 33.43 35.56 38.89 41.92 42.86 45.64 48.29 50.83 54.05 56.41
27 31.53 32.91 34.57 36.74 40.11 43.19 44.14 46.96 49.64 52.22 55.48 57.86
28 32.62 34.03 35.71 37.92 41.34 44.46 45.42 48.28 50.99 53.59 56.89 59.30
29 33.71 35.14 36.85 39.09 42.56 45.72 46.69 49.59 52.34 54.97 58.30 60.73
30 34.80 36.25 37.99 40.26 43.77 46.98 47.96 50.89 53.67 56.33 59.70 62.16
40 45.62 47.27 49.24 51.81 55.76 59.34 60.44 63.69 66.77 69.70 73.40 76.09
50 56.33 58.16 60.35 63.17 67.50 71.42 72.61 76.15 79.49 82.66 86.66 89.56
60 66.98 68.97 71.34 74.40 79.08 83.30 84.58 88.38 91.95 95.34 99.61 102.70
80 88.13 90.41 93.11 96.58 101.90 106.60 108.10 112.30 116.30 120.10 124.80 128.30
100 109.10 111.70 114.70 118.50 124.30 129.60 131.10 135.80 140.20 144.30 149.40 153.20
Chi-square distribution critical values
Table entry for p is the
critical value X* with probability
p lying to its right

Introduction: The General Linear Model

The General Linear Model is a phrase used to indicate a class of statistical models which include simple linear regression analysis.

Regression is the predominant statistical tool used in the social and natural sciences due to its simplicity and versatility.

Also called Linear Regression Analysis.

Regression Analysis

Regression has become one of the most widely used techniques in the analysis of data in the social and natural sciences __ policy research. It is closely connected to Pearson’s r.

Its a technique for analyzing quantitative data in which the relationship between two variables is expressed as a correlation (r).

Pearson’s r:

When variables are interval/ratio, by far the most common measure of correlation is Pearson’s Product Moment Correlation Coefficient, often referred to as Pearson’s r ( this measure of correlation presumes that interval variables are being used, so that even ordinal variables are not supposed to be employed).

Regression is powerful tool for summarising the nature of the relationship between variables and for making predictions of likely values of the dependent variable.



Quantitative Methods


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Simple Linear Regression:
The Basic Mathematical Model

Regression is based on the concept of the simple proportional relationship - also known as the straight line.

  • We can express this idea mathematically:

Theoretical aside: All theoretical statements of relationship imply a mathematical theoretical structure.

Just because it isn’t explicitly stated doesn’t mean that the math isn’t implicit in the language itself!

Linear Regression: the Linguistic Interpretation

In general terms, the linear model states that the dependent variable is directly proportional to the value of the independent variable.

Thus if we state that some variable Y increases in direct proportion to some increase in X, we are stating a specific mathematical model of behavior - the linear model.

Example:

Hence, if we say that the crime rate goes up as unemployment goes up, we are stating a simple linear model.

The linear model is represented by a simple picture

Chart1

1 1.6
2 1
3 5
4 7
5 4
6 7
7 9
8 8.05
9 8.6
10 9
X
Y
Simple Linear Regression

Sheet1

1 1.6
2 1
3 5
4 7
5 4
6 7
7 9
8 8.05
9 8.6
10 9

Sheet2

Sheet3

Regression Analysis

If r = 1, the line of best fit would simply be drawn straight through all of the points.

r = -1 (max. negative correlation);

r = 0 (no constant relationship);

r = 1 (max. positive correlation)

The further the points are from the line, the less accurate estimate are likely to be. Therefore, where r is low, scatter will be greater and the regression equation will provide a less accurate representation of the relationship between the two variables.


Quantitative Methods


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Regression Analysis

Summary:

Although correlation and regression are closely connected, it should be remembered that they serve different purposes.

Correlation is concerned with the degrees of relationship between variables, and

Regression with making predictions



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Correlation

  • Strength and direction of the relationship between variables
  • Scattergrams

Regression Analysis

When doing regression analysis of two variables the researcher constructs a scatter gram.

In order to understand how the line of best fit operates, it is necessary to get to grips with the simple equation that governs its operation and how we make predictions from it. Using a set of equations such as are used for representing a straight line:

y = a + bx + e

In this equation, y and x are the dependent and independent variables respectively. Two elements – a and b – refer to aspects of the line itself.

First, a is known as the intercept, which is the point at which the line cuts the vertical axis. Second, b is the slope of the line of best fit and is usually referred to as the regression coefficient.



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Best fit line, minimising sum of squared errors

  • Describing the line as in maths: y = m x + c
  • Here, ŷ = bx + a
  • ŷ : predicted value of y
  • b: slope of regression line
  • a: intercept

Residual error (ε): Difference between obtained and predicted values of y (i.e. y- ŷ).

Best fit line (values of b and a) is the one that minimises the sum of squared errors (SSerror) (y- ŷ)2

ŷ = bx + a

The Mathematical Interpretation: The Meaning of the Regression Parameters

a = the intercept

  • the point where the line crosses the Y-axis.
  • (the value of the dependent variable when all of the independent variables = 0)

b = the slope

  • the increase in the dependent variable per unit change in the independent variable (also known as the 'rise over the run')

Regression Analysis

By ‘slope’ is meant the rate at which changes in values of the independent variable (x) affect values of the dependent variable (y). In order to predict y for a given value of x, it is necessary to:

1: multiply the value of x by the regression coefficient , b; and

2: add this calculation to the intercept , a.

Finally, e is referred to as an error term, which points to the fact that a proportion of the variance in the dependent variable, y, is unexplained by the regression equation. In order to simplify the following explanation of regression, for the purposes of making predictions the error term is ignored.



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The Error Term __ e

Such models do not predict behavior perfectly.

So we must add a component to adjust or compensate for the errors in prediction.

The 'Goal' of Ordinary Least Squares

Ordinary Least Squares (OLS) is a method of finding the linear model which minimizes the sum of the squared errors.

Such a model provides the best explanation/prediction of the data.

Therefore, we wish to find the values of a and b that produce the smallest sum of squared errors.

The Simple Linear Virtue

I think we over emphasize the linear model.

It does, however, embody this rather important notion that Y is proportional to X.

As noted, we can state such relationships in simple English.

  • As unemployment increases, so does the crime rate.
  • As domestic conflict increased, national leaders will seek to distract their populations by initiating foreign disputes.

Goodness of Fit

  • Since we are interested in how well the model performs at reducing error, we need to develop a means of assessing that error reduction. Since the mean of the dependent variable represents a good benchmark for comparing predictions, we calculate the improvement in the prediction of Yi relative to the mean of Y

(the best guess of Y with no other information).

Measures of Goodness of fit

The Correlation coefficient

  • A measure of how close the residuals are to the regression line

It ranges between -1.0 and +1.0

  • It is closely related to the slope.

Equally related is the r-squared

Regression

The R-squared

  • The R-squared statistic indicates % of the variance of y is explained.

Adjusted R-squared Statistic

  • This statistic is used in a multiple regression analysis, because it does not automatically rise when an extra explanatory variable is added.

Its value depends on the number of explanatory variables

It is usually written as (R-bar squared):

Adjusted R-squared

  • In generally rises when the t-statistic of an extra variable exceeds unity (1),so does not necessarily imply the extra variable is significant.

It has the following formula (n-number of observations, k-number of parameters):

The F-test

  • The F-test is an analysis of the variance of a regression
  • It can be used to test for the significance of a group of variables or for a restriction
  • It has a different distribution to the t-test, but can be used to test at different levels of significance

When determining the F-statistic we need to collect either the Residual Sum of Squares (RSS) or the R-squared statistic

The formula for the F-test of a group of variables can be expressed in terms of either the Residual Sum of Squares (RSS) or Explained Sum of squares (ESS)

F-test of explanatory power

  • This is the F-test for the goodness of fit of a regression and in effect tests for the joint significance of the explanatory variables.

  • It is based on the R-squared statistic

It is routinely produced by most computer software packages

It follows the F-distribution, which is quite different to the t-test

F-test formula

  • The formula for the F-test of the goodness of fit is:

F-distribution

  • To find the critical value of the F-distribution, in general you need to know the number of parameters and the degrees of freedom

The number of parameters is then read across the top of the table, the d of f. from the side. Where these two values intersect, we find the critical value.

F-distribution

  • Both go up to infinity

  • If we wanted to find the critical value for F(3,4), it would be 6.6
  • The first value (3) is often termed the numerator, whilst the second (4) the denominator.

  • It is often written as:

F-test critical value

F-statistic

  • When testing for the significance of the goodness of fit, our null hypothesis is that the explanatory variables jointly equal 0.

If our F-statistic is below the critical value we fail to reject the null and therefore we say the goodness of fit is not significant.

  • The F-test is useful for testing a number of hypotheses and is often used to test for the joint significance of a group of variables

  • In this type of test, we often refer to ‘testing a restriction’

  • This restriction is that a group of explanatory variables are jointly equal to 0

Multiple regression

  • Multiple regression is used to determine the effect of a number of independent variables, x1, x2, x3 etc., on a single dependent variable, y

  • The different x variables are combined in a linear way and each has its own regression coefficient:

y = b0 + b1x1+ b2x2 +…..+ bnxn + ε

The a parameters reflect the independent contribution of each independent variable, x, to the value of the dependent variable, y.

i.e. the amount of variance in y that is accounted for by each x variable after all the other x variables have been accounted for.

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Multiple Regression Analysis

Correlational technique

  • Reliability of measures very important

Requires large sample size

Easy to get significance with large sample size

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Multiple Regression Analysis

Attempts to make causal statements of relationship

Y = X1+X2+X3

Example:

Y = dependent variable (health status/Security/Economic Growth and/or development)

X1-3 = predictors or independent variables

Health Status = Age + Gender + Smoking

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Multiple Regression Questions:

Example:

  • What is the contribution of age, gender, and smoking to health status?
  • How much of the variation in health status is accounted for by variation in age, gender, and smoking?

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Multiple Regression Analysis

  • Creates a correlation matrix.

Selects the most highly correlated independent variable with the dependent variable first.

  • Extract the variance in Y accounted for by that X variable.

Repeats the process (iterative) until no more of the variance in Y is statistically explained by the addition of another X variable.

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Health Status =
Age + Gender + Smoking

Health Status Y Age X1 r2 Gender X2 r2 Smoking X3 r2
Health Status Y 1 0.25 6% 0.04 0% 0.40 16%
Age X1 1 0.11 1% .05 0%
Gender X2 1 .20 4%
Smoking X3 1

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Multiple Regression: Shared Variance

Gender 4%

Smoking 40%

Age 25%

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Multiple Regression

  • Correlation results in a r

Multiple regressions results in an r2

R squared is the total amount of the variance in Y that is explained by the predictors, removing the overlap among the predictors.

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Multiple Regression

Types

Step-wise = based upon highest correlation, that variable is entered first (computer makes the decision), theory building

Hierarchical = choose the order of entry, forced entry, theory testing

Conclusion

  • Multiple regression analysis is similar to bi-variate analysis, however correlation between the x variables needs to be taken into account

  • The adjusted R-squared statistic tends to be used in this case

  • The F-test is used to test for joint explanatory power of the whole regression or a sub-set of the variables

We often use the F-test when testing for things like seasonal effects in the data.

Application __ Multiplier Analysis

Definition:

A multiplier, as the term is used in this lecture, is any constant term that is used in an arithmetic operation to estimate, apportion, or project some known quantity. Three uses of multipliers will be reviewed in this lecture:

The use of standards as multipliers to estimate requirements,

The use of a multiplier to make for instance, dollar values from different time periods comparable using the Consumer Price Index, and

The use of economic multipliers to project economic impacts.



Quantitative Methods


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Multiplier Analysis

Published standards:

The first type of multiplier is the published standard that is used to estimate requirements. Standards are widely used in many fields, sometimes as final rules and sometimes only as rules of thumb that may later be modified.

Examples might be the number of hospitals beds needed for a given population, the number of acres of woodland needed to support a certain species of wildlife, or the percentage of open space required in a large commercial development under certain zoning classification.



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Multiplier Analysis

Published standards:

Application __ policy:

Employees with a firm have a standard office size of 200 net square feet.

This standard can be used as multiplier to either determine the floor area needed to contain offices for 15 employees (15 x 200 = 3000 net square feet) or to determine how many offices can be placed in a floor area of 4000 net square feet (4000/200 =20 offices).



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Multiplier Analysis

Cost of Living Estimates:

The second multiplier method is the use of a multiplier to make for instance dollar values from different time periods or cost of living estimates fro different locations comparable using the Consumer Price Index (CPI).

Because of inflation, dollar values from different time periods must be adjusted before they can be directly compared. The CPI for each month in a given year tells us the inflation-adjusted value of 100 base period dollars in that month of the given year in which we are interested and for a variety of locations.

The CPI is a ratio of current prices to base period prices. It can be converted to a percentage by multiplying by 100. The CPI is sometimes referred to as the CPI-U, where the U indicates that it is an urban index.



Quantitative Methods


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Multiplier Analysis

Cost of Living Estimates:

The CPI is calculated from the prices of about 400 items in a typical ‘’market basket’’ of goods and services ( Horzwitz and Ferleger, 1980).

The CPI is a weighted average in which the weights for each of the 400 items are the percentages of total income spent on each item. The weights thus sum to 100 percent.

Indices are available for the major categories of goods and services. There are sub categories for each of these headings. For instance, under the heading ‘’apparel commodities’’ there are individual CPI’s for the subheadings: men’s and boys’, women’s and girls’, infants’ and toddlers’, foot wear, and other.



Quantitative Methods


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Multiplier Analysis

Cost of Living Estimates:

CPI values are also available specific to regions and metropolitan sizes within each region, as well as for selected major urban areas.

Application __ policy:

The CPI multiplier can be used to convert dollar values from earlier years to their present inflation-adjusted value. To find the value for all urban consumers of 1,000 1967 dollars in August 1990, we can use the CPI value as a multiplier.

We solve the equation:

$1,000/100 = X/394.1

Where X = the value in 1990. This can be converted algebraically to:

X = 394.1 ($1000)/100 = 3,941

The answer is that the 1,000 1967 dollars are equivalent in value to $3,941 in August 1990.



Quantitative Methods


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Multiplier Analysis

Cost of Living Estimates:

Application __ policy:

The CPI can be used as multiplier to compare living costs in different parts of the country. A CPI is provided for both all urban consumers and for urban wage earners for selected urban areas each month.

For instance, a family living in City A that earns $3, 500 per month can estimate the earnings needed in City B to maintain the same standard of living.

To convert the dollar values from the cost of living in one city to those in different city and correct for the effects of inflation, one must first find the CPI for all urban consumers for both cities.



Quantitative Methods


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Multiplier Analysis

Economic Multipliers:

The third type of multiplier is the economic multiplier. A variety of techniques have been developed to estimate multipliers for regional income or regional employment (Isard, 1960).

Once these multipliers have been estimated or calculated and some assumptions have been made about their reliability in the future, these economic multipliers are used to project income or employment in the future.

The earliest and still most widely used methods of developing economic multipliers are the Economic Base Study and Input – Output Analysis (Tiebout, 1962; Miernyk, 1965; Shah, 1979).



Quantitative Methods

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Multiplier Analysis

Economic Multipliers:

Both techniques assume that exports (the basic sector) drive the regional economy by bringing in money from outside the region. The multipliers are used to estimate the impact on the regional economy of new economic activity, usually additional export earnings.

The use of economic multipliers assumes that one dollar of additional export earnings will have greater impact on the regional economy than one dollar of local earnings. In theory the additional dollar of export earnings starts an infinite number of expenditure cycles in the economy.

Some portion of the income earned from the exports is spent on consumption during each of these cycles. When the money is spent on consumption in the region, business people in the region earn additional income and spend some portion of their additional income.



Quantitative Methods


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Multiplier Analysis

Economic Multipliers:

This recycling of some portion of the original new money through the infinite cycles of the multiplier produces the multiplier effect. The result is that total income in the region is greater by some multiplier of the original export earnings to the region.

Economic multiplier analysis uses these assumptions and the calculated or estimated multipliers to project the total impact on the regional economy of new money coming into the region. The multipliers might be sued to project the impact of an expansion to an existing firm in your region that produces for instances, auto –parts or to project the impact of a firm from outside your region that has decided to relocate in your region.

In both cases, economic multipliers specific to the industry being considered are needed ___ standard industrial classification (SIC code).



Quantitative Methods


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Multiplier Analysis

Economic Multipliers:

Problems:

Despite several important problems, economic multiplier (impact) analysis is widely used. The greatest problem in their use is that the calculation of economic multipliers by means of input-output analysis or an economic base study is a complex and expensive process. There are also several conceptual problems, for instance:

The multipliers can not take into account technological advances made after the multipliers were calculated but in use in the economy at the time the impact study is conducted,

The models used to calculate multipliers assume that resources used must be proportional to output, implying that changes in production levels ignore economies or dis-economies of scale in production, and,

The multipliers are assumed to have been calculated and used during periods of maximum production; this is not always true and was a problem with the multipliers produced after during the Great Depression __ National input – output table (USA).



Quantitative Methods


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Regression Analysis

When doing regression analysis of two variables the researcher constructs a scatter gram.

The idea regression is to summarise the relationship between two variables by producing a line which fits the data closely. This line is called the line of best fit. Only one line will minimise the deviations of all of the dots in a scatter diagram from the line.

Some points will appear above the line, some below and a small proportion may actually be on the line. Because only one line can meet the criterion of line of best fit, it is unlikely that it can be drawn accurately by visual inspection. This is where regression come in.

Regression procedures allow the precise line of best fit to be computed. Once we know the line of best fit, we can make predictions about likely values of the dependent variable, for particular values of the independent variable.



Quantitative Methods


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Regression Analysis

y = a + bx + e:

The straight line is drawn so as to minimize the average distance from the line to all points on the scatter-gram.

Example __ application/policy

Consider the following example. A researcher may want to know whether managers who put in extra hours after the normal working day tend to get on better in an organization that others. The researcher finds out the average amount of time a group of twenty new managers in a firm spend working on problems after normal working hours.

Two years later the managers are re-examined to find out their annual salaries. Individuals' salaries are employed as an indicator of progress, since incomes often reflect how well a person is getting on in a firm. Moreover, for these managers, extra hours work are not rewarded by overtime payments, so salaries are a real indication of progress.



Quantitative Methods


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Regression Analysis

Example __ application/policy

Let as say that the regression equation which derived from the analysis is:

y = a + bx + e

y = 7500 + 500x

The intercept, a, is 7500, that is, $7,500; the regression coefficient, b, is 500, that is, $500. The latter means that each extra hour worked produces an extra $500 on a manager’s annual salary. We can calculate the likely annual salary of someone who puts in an extra 7 hours per week as follows:

y =7500 + (500)(7)

which becomes:

y =7500 3500



Quantitative Methods



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Regression Analysis

Example __ application/policy

which becomes:

y= 11000 (that is, $11,000).

For someone who works an extra 8 hours per week, the likely salary will be $11,500, that is, 7,500 + (500)(8).

If person does not put any in extra work, the salary is likely to be $7,500, that is, 7,500 + (500)(0).

Thus, through regression, we are able to show how y changes for each additional increment of x (because the regression coefficient expresses how much more of y you get for each extra increment of x) and to predict the likely value of y for a given value of x.



Quantitative Methods

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Regression Analysis

Example __ application/policy

which becomes:

y= 11000 (that is, $11,000).

For someone who works an extra 8 hours per week, the likely salary will be $11,500, that is, 7,500 + (500)(8).

If person does not put any in extra work, the salary is likely to be $7,500, that is, 7,500 + (500)(0).

Thus, through regression, we are able to show how y changes for each additional increment of x (because the regression coefficient expresses how much more of y you get for each extra increment of x) and to predict the likely value of y for a given value of x.



Quantitative Methods


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Regression Analysis

Example __ application/policy

When a relationship is negative, the regression equation for the line of best fit will take the form y = a – bx. Thus, if a regression equation is y = 50 - 2x, each extra increment of x produces a decrease in y.

If we wanted to know the likely value of y when x = 12, we would substitute as follows:

y = 50 – 2x

y = 50 –(2)(12)

y = 50 – 24

y = 26



Quantitative Methods


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Regression Analysis

Example __ application/policy

When a line of best fit shows a tendency to be vertical and to intersect with the horizontal axis, the intercept, a, will have a minus value. This is because it will cut the horizontal axis and when extended to the vertical axis it will intercept it at a negative point. In this situation, the regression will take the form:

y = - a + bx

Supposing the equation were y = -7 + 23x; if we wanted to know the likely value of y when x = 3, we would substitute as follows:

y= - 7 + 23x

y= -7 + (23)(3)

y= -7 + 69

y= 69 -7



Quantitative Methods


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Regression Analysis

Re- cap:

As suggested at a the start of this lecture, correlation and regression are closely connected. They make identical assumptions that variables are interval/ratio and that relationships are linear.

Therefore, given a pair of related measures X and Y on each of a set of items, the term "regression" is used to characterize the manner in which one of the measures (for example the Y measures) change as the other measure ( in this case, the X measure) changes.

For any set of related measures, it is possible to specify a line that approximates the mean of the Y measures for those items with a given X measure. By revealing how the mean of the Y measures change as the various X measures change, this line is understood to describe the regression of Y on X. The regression line is the predicted value of Y for each value of X.



Quantitative Methods


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Regression Analysis

In its simplest form regression analysis involves the best straight line relationship to explain how the variation in an outcome (or dependent) variable, Y, depends on the variation in a predictor (or independent or explanatory) variable, X.

Further, r and r2 are often employed as indications of how well the regression line fits the data.

The more closely the points are clustered around the line, the higher is r (correlation).



Quantitative Methods


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Quantitative Methods

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Regression Analysis

If r = 1, the line of best fit would simply be drawn straight through all of the points.

The further the points are from the line, the less accurate estimate are likely to be. Therefore, where r is low, scatter will be greater and the regression equation will provide a less accurate representation of the relationship between the two variables.

r = -1 (max. negative correlation);

r = 0 (no constant relationship);

r = 1 (max. positive correlation)



Quantitative Methods


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Regression Analysis

Summary:

Although correlation and regression are closely connected, it should be remembered that they serve different purposes.

Correlation is concerned with the degrees of relationship between variables, and

Regression with making predictions



Quantitative Methods

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Regression Analysis

Summary__ Applications to Time Series:

If the independent variable X is time, the data shows the values of Y at various times. Data arranged according to time are called time series.

The regression line or curve and is often used for purposes of estimation, prediction or forecasting.

Linear regression is a GLM that models the effect of one independent variable, x, on one dependent variable, y

Multiple Regression models the effect of several independent variables, x1, x2 etc, on one dependent variable, y



Quantitative Methods


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Quantitative Methods


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The Chi Square Statistic

Types of Data:

There are basically two types of random variables and they yield two types of data: numerical and categorical. A chi square (X2) statistic is used to investigate whether distributions of categorical variables differ from one another.

Categorical variable

Basically categorical variable yield data in the categories and numerical variables yield data in numerical form. Responses to such questions as "What is your major?" or Do you own a car?" are categorical because they yield data such as "biology" or "no."



Quantitative Methods


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The Chi Square Statistic

Types of Data:

Numerical

In contrast, responses to such questions as "How tall are you?" or "What is your G.P.A.?" are numerical. Numerical data can be either discrete or continuous.



Quantitative Methods


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The Chi Square Statistic

  • Discrete

A type of data is discrete if there are only a finite number of values

possible or if there is a space on the number line between each 2 possible

values.

Example:

A 5 question quiz is given in a Math class. The number of correct answers on a student's

quiz is an example of discrete data. The number of correct answers would have to be one of

the following : 0, 1, 2, 3, 4, or 5.

There are not an infinite number of values, therefore this data is discrete. Also, if we

were to draw a number line and place each possible value on it, we would see a space between

each pair of values.


Quantitative Methods

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The Chi Square Statistic

  • Discrete

Example: __ (policy)

In order to obtain a taxi license in Toronto, a person must pass a written exam regarding different locations in the city. How many times it would take a person to pass this test is also an example of discrete data. A person could take it once, or twice, or 3 times, or 4 times, or… . So, the possible values are 1, 2, 3, … . There are infinitely many possible values, but if we were to put them on a number line, we would see a space between each pair of values.


Discrete data usually occurs in a case where there are only a certain number of values, or when we are counting something (using whole numbers).



Quantitative Methods


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The Chi Square Statistic

  • Continuous Data

Continuous data makes up the rest of numerical data. This is a type of

data that is usually associated with some sort of physical measurement.

Example:

The height of trees at a nursery is an example of continuous data. Is it

possible for a tree to be 76.2" tall? Sure. How about 76.29"? Yes. How about

76.2914563782"? The possibilities depends upon the accuracy of our

measuring device.



Quantitative Methods


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The Chi Square Statistic

  • Continuous Data

One general way to tell if data is continuous is to ask yourself if it is

possible for the data to take on values that are fractions or decimals. If your

answer is yes, this is usually continuous data.

Example __(policy)

The length of time it takes for a light bulb to burn out is an example of

continuous data. Could it take 800 hours? How about 800.7? 800.7354?

The answer to all 3 is yes.



Quantitative Methods


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The Chi Square Statistic

In class exercise

  • Classify each set of data as discrete or continuous.

1) The number of suitcases lost by an airline.

2) The height of corn plants.

3) The number of ears of corn produced.

4) The number of green M&M's in a bag.

5) The time it takes for a car battery to die.

6) The production of tomatoes by weight.




Quantitative Methods


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The Chi Square Statistic

In class exercise

Answers:

Discrete___ The number of suitcases lost must be a whole number.

2) Continuous___ The height of corn plants can take on infinitely many values (any decimal is

possible).

3) Discrete___ The number of ears of corn must be a whole number.





Quantitative Methods


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The Chi Square Statistic

In class exercise

Answers:


4) Discrete__ The number of green M&M's must be a whole number.

5) Continuous__ The amount of time can take on infinitely many values (any decimal is

possible).

6) Continuous__ The weight of the tomatoes can take on infinitely many values (any decimal is

possible).




Quantitative Methods


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The Chi Square Statistic

  • Discrete or continuous

The table below may help you see the differences between these two variables.

Data Type   Question Type Possible Responses

Categorical   What is your sex? male or female

Numerical Discrete- How many cars two or three

do you own?

Numerical Continuous - How tall are you?   72 inches

Notice that discrete data arise from a counting process, while continuous data arise from a measuring process.



Quantitative Methods


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Non-parametric Statistics

  • A special class of hypothesis tests
  • Used when assumptions for parametric tests are not met
  • Review: What are the assumptions for parametric tests?

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Assumptions for Parametric Tests

  • Dependent variable is a scale variable  interval or ratio
  • If the dependent variable is ordinal or nominal, it is a non-parametric test

  • Participants are randomly selected
  • If there is no randomization, it is a non-parametric test

  • The underlying population distribution is normal
  • If the shape is not normal, it is a non-parametric test

When to Use Non-parametric Tests

  • When the dependent variable is nominal
  • What are ordinal, nominal, interval, and ratio scales of measurement?

  • Used when either the dependent or independent variable is ordinal

  • Used when the sample size is small

  • Used when underlying population is not normal

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Limitations of Non-parametric Tests

  • Cannot easily use confidence intervals or effect sizes
  • Have less statistical power than parametric tests

  • Nominal and ordinal data provide less information
  • More likely to commit type II error
  • Review: What is type I error? Type II error?

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Chi-Square Test for Goodness-of-Fit

  • Non-parametric test when we have one nominal variable

  • These variables, also called "attribute variables" or "categorical variables," classify observations into a small number of categories. A good rule of thumb is that an individual observation of a nominal variable is usually a word, not a number

  • Examples of nominal variables include sex (the possible values are male or female), genotype (values are AA, Aa, or aa), or ankle condition (values are normal, sprained, torn ligament, or broken)

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Chi-Square Test for Goodness-of-Fit

  • Nonparametric test when we have one nominal variable
  • Measurement v. Nominal: Imagine recording each observation in a lab notebook. If you record a number (width, height, speed, errors) it’s a measurement, if you record a label it’s nominal (sex, popularity, beauty)

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Examples of When to Use Chi-Square

  • The observed counts of numbers of observations in each category are compared with the expected counts, which are calculated using some kind of theoretical expectation, such as a 1:1 sex ratio, or 4:2:1 population density in following example.

  • Example: looking at an area of shore that had 59% of the area covered in sand, 28% mud and 13% rocks (4:2:1); if seagulls were standing in random places, your null hypothesis would be that 59% of the seagulls were standing on sand, 28% on mud and 13% on rocks (4:2:1).

Examples of Chi-Square

  • Does the count of the Observed match the count of the Expected?
  • Mendel crossed peas that were heterozygotes for Smooth/wrinkled, where Smooth is dominant. The expected ratio in the offspring is 3 Smooth: 1 wrinkled. He observed 423 Smooth and 133 wrinkled.

  • The expected frequency of Smooth is calculated by multiplying the sample size (556) by the expected proportion (0.75) to yield 417. The same is done for green to yield 139. The number of degrees of freedom when an extrinsic hypothesis is used is the number of values of the nominal variable minus one. In this case, there are two values (Smooth and wrinkled), so there is one degree of freedom.

  • The result is chi-square=0.35, 1 d.f., P=0.557, indicating that the null hypothesis cannot be rejected; there is no significant difference between the observed and expected frequencies.

Examples of Chi-Square

  • Does the count of the Observed match the count of the Expected?
  • Mannan and Meslow (1984) studied bird foraging behavior in a forest in Oregon. In a managed forest, 54% of the canopy volume was Douglas fir, 40% was ponderosa pine, 5% was grand fir, and 1% was western larch.
  • They made 156 observations of foraging by red-breasted nuthatches; 70 observations (45% of the total) in Douglas fir, 79 (51%) in ponderosa pine, 3 (2%) in grand fir, and 4 (3%) in western larch.
  • The biological null hypothesis is that the birds forage randomly, without regard to what species of tree they're in; the statistical null hypothesis is that the proportions of foraging events are equal to the proportions of canopy volume. The difference in proportions is significant (chi-square=13.593, 3 d.f., P=0.0035).


How the test works

  • The test statistic is calculated by taking an observed number (O), subtracting the expected number (E), then squaring this difference. The larger the deviation from the null hypothesis, the larger the difference between observed and expected is.

  • Squaring the differences makes them all positive. Each difference is divided by the expected number, and these standardized ratios are summed: the more differences between what you would expect and what you get the bigger the number.

Chi-Square Test for Goodness-of-Fit

  • The six steps of hypothesis testing

Question: Are the best soccer players born early rather than later in the year ?

1. Identify

2. State the hypotheses

3. Characteristics of the comparison distribution

4. Critical values

5. Calculate

6. Decide

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Chi-Square Test for Goodness-of-Fit

  • The six steps of hypothesis testing

Identify Pop. Distribution & Assumptions

Two populations, one distribution that matches expected outcomes and another where distribution matches observed outcomes. E.g., great soccer players are born evenly throughout year, great soccer players born in first half of year.

Comparison distribution is chi-square

First assumption, variable of interest is nominal, birth month. Second, independence of observation, that is each observation fits in only one category, no soccer player has two birth months. Third, random selection of pop ( in this case, they are only Germans, and only elite). Fourth, large enough sample size, ideally 5 times the number of cells (in this case N= 56 > 10 (2 x 5).

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Chi-Square Test for Goodness-of-Fit

State the hypotheses: does the Observed count of elite soccer player Birth Months match the Expected count of elite soccer player Birth Months

Null: Match

Alternative: No match

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Chi-Square Test for Goodness-of-Fit

  • Characteristics of the comparison distribution

NB: where k is the number of groups

  • Only two categories of soccer players

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Chi-Square Test for Goodness-of-Fit

  • Critical values

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Chi-Square Test for Goodness-of-Fit

  • Calculate

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Chi-Square Test for Goodness-of-Fit

  • Calculate

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Making a Decision

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  • Evenly divided expected frequencies
  • Can you think of examples where you would expect evenly divided expected frequencies in the population?

A more typical Chi-Square

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  • Chi-square test for independence
  • Analyzes 2 nominal variables
  • The six steps of hypothesis testing

1. Identify

2. State the hypotheses

3. Characteristics of the comparison distribution

4. Critical values

5. Calculate

6. Decide

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The Cutoff for a Chi-Square Test for Independence

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The Decision

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Cramer’s V (phi)

  • The effect size for chi-square test for independence

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Graphing Chi-Squared Percentages

Relative Risk

  • We can quantify the size of an effect with chi square through relative risk, also called relative likelihood.

  • By making a ratio of two conditional proportions, we can say, for example, that one group is three times as likely to show some outcome or, conversely, that the other group is one-third as likely to show that outcome.

Adjusted Standardized Residuals

  • The difference between the observed frequency and the expected frequency for a cell in a chi-square research design, divided by the standard error; also called adjusted residual.

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Formulae

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Determining the Cutoff for a Chi-Square Statistic

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The Chi Square Statistic

The Chi Square statistic compares the tallies or counts of categorical responses between two (or more) independent groups. (note: Chi square tests can only be used on actual numbers and not on percentages, proportions, means, etc.)

In the nutshell:

Used in tests of correlation i.e. measuring the strength of associations between variables

Test associations in one or more groups

Comparing actual observed numbers in each group with those that would be expected according to theory or simple by chance.


Quantitative Methods


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The Chi Square Statistic

2 x 2 Contingency Table

There are several types of chi square tests depending on the way the data was collected and the hypothesis being tested. We'll begin with the simplest case: a 2 x 2 contingency table.

If we set the 2 x 2 table to the general notation shown below in Table 1, using the letters a, b, c, and d to denote the contents of the cells, then we would have the following table:



Quantitative Methods


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The Chi Square Statistic

Table 1. General notation for a 2 x 2 contingency table.

Variable 1

Variable 2 Data type 1   Data type 2   Totals

Category 1   a b a + b

Category 2  c d c + d

Total a + c b + d a + b + c + d = N


Quantitative Methods


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The Chi Square Statistic

Table 1. General notation for a 2 x 2 contingency table.

Variable 1

Variable 2 Data type 1   Data type 2   Totals

Category 1   a b a + b

Category 2  c d c + d

Total a + c b + d a + b + c + d = N

For a 2 x 2 contingency table the Chi Square statistic is calculated by the formula:


Quantitative Methods


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Note:

notice that the four components of the denominator are the four totals from the table columns and rows.

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X2 = (ad – bc)2 (a + b + d)

(a + b) (c + d) (b + d) (a + c)


Quantitative Methods


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The Chi Square Statistic

2 x 2 Contingency Table

Example:

Suppose you conducted a drug trial on a group of animals and you hypothesized that the animals receiving the drug would survive better than those that did not receive the drug. You conduct the study and collect the following data:

  • Ho: The survival of the animals is independent of drug treatment.

  • H1: The survival of the animals is associated with drug treatment.



Quantitative Methods

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The Chi Square Statistic

Table 2. Number of animals that survived a treatment.


  Dead   Alive Total

Treated   36   14   50

Not treated   30   25   55

Total   66   39   105

Applying the formula above we get:

  • Chi square = 105[(36)(25) - (14)(30)]2 / (50)(55)(39)(66) = 3.418


Quantitative Methods


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The Chi Square Statistic

Table 2. Number of animals that survived a treatment.

Before we can proceed we need to know how many degrees of

freedom we have.

Degrees of freedom (df)

Degrees of freedom is a way of keeping score. A data set contains a number of observations, say, n. They constitute n individual pieces of information. These pieces of information can be used to estimate either parameters or variability. In general, each item being estimated costs one degree of freedom. The remaining degrees of freedom are used to estimate variability. All we have to do is count properly.


 


Quantitative Methods


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The Chi Square Statistic

Table 2. Number of animals that survived a treatment.

Before we can proceed we need to know how many degrees of freedom we have.

Degrees of freedom (df)

Some estimates are based on more information than others. For example, an estimate of the variance based on a sample size of 100 is based on more information than an estimate of the variance based on a sample size of 5. The degrees of freedom (df) of an estimate is the number of independent pieces of information on which the estimate is based.


 


Quantitative Methods


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The Chi Square Statistic

Table 2. Number of animals that survived a treatment.

Before we can proceed we need to know how many degrees of freedom we have.

Degrees of freedom (df)

  • As an example, let's say that we know that the mean height of Martians is 6 and wish to estimate the variance of their heights. We randomly sample one Martian and find that its height is 8.
  • Recall that the variance is defined as the mean squared deviation of the values from their population mean. We can compute the squared deviation of our value of 8 from the population mean of 6 to find a single squared deviation from the mean.


Quantitative Methods


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The Chi Square Statistic

Table 2. Number of animals that survived a treatment.

Before we can proceed we need to know how many degrees of freedom we have.

Degrees of freedom (df)

This single squared deviation from the mean (8-6)2 = 4 is an estimate of the mean squared deviation for all Martians. Therefore, based on this sample of one, we would estimate that the population variance is 4.

This estimate is based on a single piece of information and therefore has 1 df. If we sampled another Martian and obtained a height of 5, then we could compute a second estimate of the variance (5-6)2 = 1.
 


Quantitative Methods


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The Chi Square Statistic

Table 2. Number of animals that survived a treatment.

Before we can proceed we need to know how many degrees of freedom we have.

Degrees of freedom (df)

We could then average our two estimates (4 and 1) to obtain an estimate of 2.5. Since this estimate is based on two independent pieces of information, it has two degrees of freedom.

The two estimates are independent because it is based on two independently and randomly selected Martians. The estimates would not be independent if after sampling one Martian, we decided to choose its brother as our second Martian.


 


Quantitative Methods


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The Chi Square Statistic

Table 2. Number of animals that survived a treatment.

Before we can proceed we need to know how many degrees of freedom we have.

Degrees of freedom (df)

The estimates would not be independent if after sampling one Martian, we decided to choose its brother as our second Martian.

As you are probably thinking, it is pretty rare that we know the population mean when we are estimating the variance. Instead, we have to first estimate the population mean (μ) with the sample mean (M). The process of estimating the mean affects our degrees of freedom as shown below.


 


Quantitative Methods

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The Chi Square Statistic

Table 2. Number of animals that survived a treatment.

Before we can proceed we need to know how many degrees of freedom we have.

Degrees of freedom (df)

Returning to our problem of estimating the variance in Martian heights, let's assume we do not know the population mean and therefore we have to estimate it from the sample.

We have sampled two Martians and found that their heights are 8 and 5. Therefore M, our estimate of the population mean, is

  • M = (8+5)/2 = 6.5.


 


Quantitative Methods


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The Chi Square Statistic

Table 2. Number of animals that survived a treatment.

Before we can proceed we need to know how many degrees of freedom we have.

Degrees of freedom (df)

We can now compute two estimates of variance by computing

  • Estimate 1 = (8-6.5)2 = 2.25
  • Estimate 2 = (5-6.5)2 = 2.25

Now for the key question: Are these two estimates independent? The answer is no because each height contributed to the calculation M. Since the first Martian's height of 8 influenced M, it also influenced Estimate 2.



Quantitative Methods


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The Chi Square Statistic

Table 2. Number of animals that survived a treatment.

Before we can proceed we need to know how many degrees of freedom we have.

Degrees of freedom (df)

In general, the degrees of freedom for an estimate is equal to the number of values minus the number of parameters estimated en route to the estimate in question. In the Martians example, there are two values(8 and 5) and we had to estimate one parameter on the way to estimating the parameter of interest.

Therefore, the estimate of variance has 2 -1 =1 degrees of freedom. If we had sampled 12 Martians, then our estimate of variance would have had 11 degrees of freedom. Therefore the degrees of freedom of an estimate of variance is equal to N -1 where N is the number of observations.


Quantitative Methods

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The Chi Square Statistic

Table 2. Number of animals that survived a treatment.

Before we can proceed we need to know how many degrees of freedom we have.

Degrees of freedom (df)

When a comparison is made between one sample and another, a simple rule is that the degrees of freedom equal (number of columns minus one) x (number of rows minus one) not counting the totals for rows or columns. For our data this gives (2-1) x (2-1) = 1.



Quantitative Methods


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The Chi Square Statistic

Table 2. Number of animals that survived a treatment.

Before we can proceed we need to know how many degrees of freedom we have.

Degrees of freedom (df)

We now have our chi square statistic (x2 = 3.418), our predetermined alpha level of significance (0.05), and our degrees of freedom (df =1)

Alpha level of significance

Therefore, the 0.01 level is more conservative than the 0.05 level. The Greek letter alpha (α) is sometimes used to indicate the significance level.


Quantitative Methods
Session 4: Description

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The Chi Square Statistic

Table 2. Number of animals that survived a treatment.

Before we can proceed we need to know how many degrees of freedom we have.

Degrees of freedom (df)

Entering the Chi square distribution table with 1 degree of freedom and reading

along the row we find our value of x2 (3.418) lies between 2.706 and 3.841.


Quantitative Methods


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The Chi Square Statistic

Table 3. Chi Square distribution table.

Df 0.5 0.10 0.05 0.02 0.01 0.001

0.455 2.706 3.841 5.412 6.635 10.827

2 1.386 4.605 5.991 7.824 9.210 13.815

2.366 6.251 7.815 9.837 11.345 16.268

4 3.357 7.779 9.488 11.668 13.277 18.465

4.351 9.236 11.070 13.388 15.086 20.517



Quantitative Methods


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Correlation coefficient

A coefficient of correlation is a mathematical measure of how much one number

(such as a share price) can expected to be influenced by changes in another. It is

closely related to covariance.

A correlation coefficient of 1 means that the two numbers are perfectly

correlated: if one grows so does the other, and the change in one is a multiple of the

change in the other.

A correlation coefficient of -1 means that the numbers are perfectly inversely

correlated. If one grows the other falls. The growth in one is a negative multiple of

the growth in the other.



Quantitative Methods


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Correlation coefficient

A correlation coefficient of zero means that the two numbers are not related.

The closer the correlation coefficient is to zero the greater the uncertainty, and low correlation coefficients means that the relationship is not certain enough to be useful.

The description above is of is a relationship between two variables. It is also possible to calculate correlations between many variables. Adding more variables should increase the correlation; any variables that do not significantly improve the correlation should be excluded.



Quantitative Methods


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Covariance

The covariance of two variables (numbers measuring something) is a measure

of the relationship between them. It closely related to the correlation and

calculated as an intermediate step in calculating the correlation.

The covariance of two numbers is the arithmetic mean, over all values of x1, and

the corresponding values of x2, of:

(x1 - μ1)(x2 - μ2)

where

x1 is the value of one variable

x2 is the value of the other variable

μ1 is the arithmetic mean of of x1 and

μ2 is the arithmetic mean of of x2.



Quantitative Methods


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Correlation

The correlation of x1 and x2 is:

(cov(x1, x2))/(σ1σ2)

where

cov(x1,x2) is the covariance of x1 and x2

σ1 is the standard deviation of x1 and

σ2 is the standard deviation of x2.


Quantitative Methods


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Correlation

Note:

Standard deviation

The standard deviation is a measure of how spread out a set of numbers are. The

standard deviation of a set of numbers is the square root of their variance.

  • Variance is usually denoted by σ2 and the standard deviation by σ, and:

σ2 = 1/n Σ(xi - μ)2

where

xi is one of n numbers and

μ is the arithmetic mean all n numbers x.


Quantitative Methods


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Correlation

Note:

Standard deviation

The most common use of the standard deviation in finance is to measure the

risk of holding a security or portfolio. We first need the expected price:

E[S] =ΣS ip(Si)

where

S is a price

and

p(Si) is the probability that S will be the actual price.


Quantitative Methods

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Correlation

Note:

Standard deviation

  • Denoting the variance of S, Var(S):

Var(S) = Σ(Si - E[S])2p(Si)

Var(S) is a measure of volatility. Its square root (the standard deviation) is the most widely used measure of volatility.



Quantitative Methods


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Correlation Analysis

Gamma

Definition:

Correlation coefficients are used to determine whether a relationship exists between sets of two variables. In other words, measures of correlation help us determine whether there is a pattern to a set of data. When examining two variables, one independent and one dependent (i.e., the predictor and the predicted variables), a useful question to ask is, ‘’How strong is the relationship between these variables?

Correlation coefficients give a numerical value that summarizes the strength and direction of the relationship between two variables.



Quantitative Methods


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Correlation Analysis

Gamma

Definition:

Various correlation coefficients’ are used to analyze data, depending on whether the data are nominal, ordinal, interval, or ratio in scale. In the quick analysis, gamma or Yule’s Q is useful correlation measure when data can be structured in ordinal form (Davis, 1971).

This is the case with most public policy data, especially survey data. Gamma can also be applied to higher level (interval or ratio) data that have been categorized as ordinal data, for instance, income data that have been grouped into high, middle, and low income categories.



Quantitative Methods

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Correlation Analysis

Gamma

Definition:

Gamma is especially useful when data are available in tabular form and we wish to determine quickly whether there is a correlation between the variables.

Method:

Gamma is a correlation coefficient that ranges from – 1.0 to + 1.0, with

0.0 indicating no relationship between the variables;

+ 1.0 indicating a perfect positive relationship, and

– 1.0 indicating a perfect negative relationship.



Quantitative Methods


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Correlation Analysis

Gamma

Method:

Since the data are ordered, that is, for each variable its values are scaled from low to high, a positive correlation means that the values of the variables are consistent:

High values on one variable are associated or correlated with high values on the other variable.

A negative correlation means that high values on one variable are associated with low values on the other variable



Quantitative Methods


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Correlation Analysis

Gamma

Method:

Most of us already have a general understanding of correlation or association. For example, we probably believe that people with more years of education earn higher incomes. This, then would be described as an hypothesis that asserts that there is a positive correlation between education and income.

A correlation coefficient can be used to tell us how strong this association happens to be, if it indeed exists. Gamma is one of several measures of correlation that can be used for this purpose.

We propose its use because it can be computed quickly with paper and pencil and its meaning is easily understood and conveyed.



Quantitative Methods


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Correlation Analysis

Gamma

Method:

Gamma is most easily computed from tabular data. Assume we have two variables, each with two values. The data should be laid out as in Table 1, where the letters a, b, c, and d simply label the cells. Tabular analysis: variable one might be Age and variable two would be Approval or Disapproval of Night Soccer.

Table 1.

The Layout for Gamma

Variable Two

Variable One Low High

High c b

Low a d



Quantitative Methods


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Correlation Analysis

Gamma

Method:

The value of gamma is determined by computing the relationship between the number of pairs of observations having the same ranking on the two variables and the number of pairs of observations having the opposite rankings on the two variables. The relationship is as follows:

(same – opposite)

Gamma =

(same + opposite)

The observations falling into the cells identified above as a and b have the same ranking (high on both variables and low on both, respectively).



Quantitative Methods
Session 8a: Explanation

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Correlation Analysis

Gamma

Method:

The observations falling into cells c and d have the opposite ranking (a high value on one variable and a low value on the other). The following formula can be used to compute gamma, where a, b, c, and d are replaced by the number of observations in the respective cells. The symbol for Gamma is the Greek letter γ:

(a x b) – (c x d)

Gamma =

(a x b) + (c x d)



Quantitative Methods


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Correlation Analysis

Gamma

Example __policy:

Assume data are being presented to the city council about citizens who support and oppose installing a new concert stage in the city park. We are told that 51 percent of the people living near the park support installing the stage. We discover, however, that the data were collected for both homeowners and renters. Consequently, the data can be structured as shown in table 2.



Quantitative Methods


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Correlation Analysis

Gamma

Example __policy:

Table 2.

Resident Attitudes about the Concert Stage

Respondent’s Homeownership Position on Stage

Status Against For

Renter 46 160

Owner 150 44

Total 196 204



Quantitative Methods


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Correlation Analysis

Gamma

Example __policy:

When we examine the data in this way, we immediately see that homeownership is related to one’s position toward installing the stage.

(150 x 160) – (46 – 44)

Gamma =

(150 x 160) + (46 x 44)

24,000 – 2,024 21, 976

= = = + .84

24,000 + 2, 024 26,024



Quantitative Methods

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Correlation Analysis

Gamma

Example __policy:

The gamma of +.84 indicates a very strong positive relationship between home ownership status and one’s position toward installing the concert stage. Although the majority of respondents favoured installing the stage, homeowners, while in the minority in the area, overwhelming oppose the installation of the stage.

Such data and their interpretation would likely be of great interest to city council members.



Quantitative Methods

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Correlation Analysis

Gamma

Example __policy:

How do you decide whether a correlation is low, moderate, or high?

The terms used to describe various numerical values are arbitrary and depend upon the conventions developed in various fields. James Davis (1971) has proposed the categories given in Table 3.



Quantitative Methods


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Correlation Analysis

Gamma

Example __policy:

Terms for Values of Gamma

Value of Gamma Appropriate Phrase

+.70 or higher A very strong positive association

+.50 to +.69 A very substantial positive association

+.30 to +.49 A moderate positive association

+.10 to +.29 A low positive association

.00 No association

-.01 to -.09 A negligible negative association

-.10 to -.29 A low negative association

-.30 to -.49 A moderate negative association

-.50 to -.69 A substantial negative association

-.70 or lower A very strong negative association



Quantitative Methods
S

Source: James A. Davis, Elementary Survey Analysis, Prentice- Hall, Inc., Englewood Cliffs, New Jersey, 1971. p.49.

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Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design

Data Collection Methods__ methods of data collection to be used in the final study.

Implement:

  • Collect and check ‘data’
  • Encode the data
  • Put ‘data’ into matrix


The Research Proposal

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design

Data Collection Methods__ methods of data collection to be used in the final study.

Analyse:

  • Analyse and extrapolate frequency distribution, average, mean, mode, standard deviation, etc.
  • Carry out a statistical test of hypotheses (to establish the relationships between variables)



The Research Proposal

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design

Data Collection Methods__ methods of data collection to be used in the final study.

Defining the objectives of the survey:

This is the case:

  • When we want to estimate with a certain degree of accuracy e.g. How many farms in an area fall in each of a number of categories (e.g. 0-5 ha,>5-10 ha, >10 ha; dairy vs. fruits vs. pigs; female or male headed; etc.).



The Research Proposal

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design

Data Collection Methods__ methods of data collection to be used in the final study.

Sample Selection:

Sampling is the process of selecting units (e.g., people, organizations) from a population of interest so that by studying the sample we may fairly generalize our results back to the population from which they were chosen.



The Research Proposal

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design

Data Collection Methods__ methods of data collection to be used in the final study.

Sample Selection: __ population

In applied social research, we are interested in generalizing to specific groups. The group you wish to generalize to is often called the population in your study.



The Research Proposal

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design

Data Collection Methods__ methods of data collection to be used in the final study.

Sample Selection: __ population

Example: Agriculture and Mining

Population of interest will include small and large-scale miners and, the peasant and plantation agricultural house-holds within the western region of Ghana.



The Research Proposal

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design

Data Collection Methods__ methods of data collection to be used in the final study.

Sample Selection: __ sample frame or list of accessible members

The listing of the accessible population from which you'll draw your sample is called the sampling frame.

Ministry of Agriculture/Ministry of Mine’s and Energy



The Research Proposal

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design

Data Collection Methods__ methods of data collection to be used in the final study.

Sampling techniques in research__Probability Sampling

  • Humans have long practiced various forms of random selection, such as picking a name out of a hat.

  • Non-probability Sampling that non-probability sampling does not involve random selection as probability sampling does.


The Research Proposal

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design

Data Collection Methods__ methods of data collection to be used in the final study.

Sampling techniques in research__Random Sampling

The simplest form of random sampling is called simple random sampling.



The Research Proposal

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design

Data Collection Methods__ methods of data collection to be used in the final study.

Sampling techniques in research__Stratified Random Sampling

sometimes called proportional or quota random sampling, dividing your population into homogeneous subgroups

Example: mining and agriculture


The Research Proposal

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design

Data Collection Methods__ methods of data collection to be used in the final study.

Sampling techniques in research__Multi-stage sampling

Target population exists at different levels:



The Research Proposal

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design

Data Collection Methods__ methods of data collection to be used in the final study.

Sampling techniques in research__Multi-stage sampling

  • In a three-stage sample the sampled strata might be districts, communities and households.

Example: voters __National – regional – district – village


The Research Proposal

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design

Data Collection Methods__ methods of data collection to be used in the final study.

Sampling techniques in research__Systematic Random Sampling

Similar to simple random sampling, but instead of selecting random numbers from tables, you move through list (sample frame) picking every nth name.


The Research Proposal

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design

Data Collection Methods__ methods of data collection to be used in the final study.

Sampling techniques in research__Systematic Random Sampling

Here are the steps you need to follow:

  • number the units in the population from 1 to N
  • decide on the n (sample size) that you want or need
  • k = N/n = the interval size



The Research Proposal

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design

Data Collection Methods__ methods of data collection to be used in the final study.

Sampling techniques in research__Systematic Random Sampling

Sample Fraction

E.g. for a population of 500 and a sample of 100, the sampling fraction is 1/5 i.e. you will select one person out of every five in the population.



The Research Proposal

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design

Data Collection Methods__ methods of data collection to be used in the final study.

Sampling techniques in research__Cluster (Area) Random Sampling

... sampling in which sampling units (that is, households) at some point in the selection process are collections, or clusters, of population elements (or households)



The Research Proposal

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Quantitative Methods
T

In cluster sampling, we follow these steps:

  • divide population into clusters (usually along geographic boundaries)

  • randomly sample clusters

  • measure all units within sampled clusters

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Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design

Data Collection Methods__ methods of data collection to be used in the final study.

Sampling techniques in research__Cluster (Area) Random Sampling

Example:

20 minutes per house-hold to administer a questionnaire

Five minutes to move in-between house-holds



The Research Proposal

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design

Data Collection Methods__ methods of data collection to be used in the final study.

Sampling techniques in research__Cluster (Area) Random Sampling

Example:

Working 6.5 hours per day__ researcher covers 15 houses per day


The Research Proposal

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design

Data Collection Methods__ methods of data collection to be used in the final study.

Sampling techniques in research__Cluster (Area) Random Sampling

Example:

﴾ 6.5 hours x 60 minutes ﴿ ÷ 25 minutes/household ~ 15 This become the cluster size.

day hour



The Research Proposal

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design

Data Collection Methods__ methods of data collection to be used in the final study.

Sampling techniques in research__Cluster (Area) Random Sampling

The total number of clusters is given by the formula:

Total number of clusters = Total number of households in sample

Cluster size



The Research Proposal

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design

Data Collection Methods__ methods of data collection to be used in the final study.

Confidence interval: + (plus or minus%)

  • Margin of Error envisaged for the research
  • decreases as the sample size increases



The Research Proposal

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design

Data Collection Methods__ methods of data collection to be used in the final study.

Confidence Level:

  • tells you how sure you can be
  • Most researchers use the 95% confidence level.



The Research Proposal

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design ___The Sample Size

Depends on three factors:

The estimated prevalence of the variable of interest _ e.g. Pregnant women within a theatre of operation;

The desired level of confidence;

The acceptable margin of error



The Research Proposal

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design ____ The Sample Size


Simple random sample, the sample size required is calculated according to this formula:

n = t2 x p(1-p)

e2

Where:

n = required sample size

t = confidence level at 95%

P = estimated prevalence of pregnancy in the study area

e = margin of error at 5% ( standard value of 0.05)


The Research Proposal

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design ____ The Sample Size


Simple random sample, the sample size required is calculated according to this formula:

n = t2 x p(1-p)

e2

p (data) can be taken obtain from published reports: Health centres, Government statistical reports/UNDP etc. Example 20% of national population.

Population of the study area is 124, 050



The Research Proposal

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design ____ The Sample Size


Simple random sample, the sample size required is calculated according to this formula:

Thus n is:

n = 1.962 x 0.2 (1 – 0.2)/0.052

3.8416 x 0.21/ 0.0025

n= 322


The Research Proposal

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Determining Sample Size: How to Ensure You Get the Correct Sample Size (Cont’)

Before you can calculate a sample size, you need to determine a few things about the target population and the sample you need:

Population Size — How many total people fit your demographic? For instance, if you want to know about mothers living in the Accra, your population size would be the total number of mothers living in the Accra. Don’t worry if you are unsure about this number. It is common for the population to be unknown or approximated.

Margin of Error (Confidence Interval) — No sample will be perfect, so you need to decide how much error to allow. The confidence interval determines how much higher or lower than the population mean you are willing to let your sample mean fall. If you’ve ever seen a political poll on the news, you’ve seen a confidence interval. It will look something like this: “68% of voters said yes to Proposition Z, with a margin of error of +/- 5%.”

Source: Scott Smith, (2013). http://www.qualtrics.com/blog/determining-sample-size/



Determining Sample Size: How to Ensure You Get the Correct Sample Size (Cont’)

3. Confidence Level — How confident do you want to be that the actual mean falls within your confidence interval? The most common confidence intervals are 90% confident, 95% confident, and 99% confident.

4. Standard of Deviation — How much variance do you expect in your responses? Since we haven’t actually administered our survey yet, the safe decision is to use .5 – this is the most forgiving number and ensures that your sample will be large enough.

Now that we have these values defined, we can calculate our needed sample size.

Your confidence level corresponds to a Z-score. This is a constant value needed for this equation. Here are the z-scores for the most common confidence levels:

90% – Z Score = 1.645

95% – Z Score = 1.96

99% – Z Score = 2.326

If you choose a different confidence level, use this Z-score table to find your score.



Determining Sample Size: How to Ensure You Get the Correct Sample Size (Cont’)

Next, plug in your Z-score, Standard of Deviation, and confidence interval into this equation:*

Necessary Sample Size = (Z-score)² – StdDev*(1-StdDev) / (margin of error)²

Here is how the math works assuming you chose a 95% confidence level, .5 standard deviation, and a margin of error (confidence interval) of +/- 5%.

((1.96)² x .5(.5)) / (.05)²

(3.8416 x .25) / .0025

.9604 / .0025

384.16

385 respondents are needed

Voila!

  • You’ve just determined your sample size.
  • If you find your sample size is too large to handle, try slightly decreasing your confidence level or increasing your margin of error – this will increase the chance for error in your sampling, but it can greatly decrease the number of responses you need.

Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design

Questionnaire Design

  • A “questionnaire” needs to be designed to allow comparison of answers from different respondents. The questionnaire should therefore be standardised.



The Research Proposal

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design

Questionnaire Design

Questionnaire Design

  • “Open” questions are those to which any answer can be accepted: e.g. how is the management of the irrigation scheme organized? What is your opinion of the quagmire/war in Iraq and/or Afghanistan.




The Research Proposal

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design

Questionnaire Design

__Questionnaire Design

  • “Closed” questions are those to which a definite answer is required: e.g. “How many rice varieties did you plant on your farm this year”. How many people (Refugees) haven been displaced in the quagmire/war in Iraq and/or Afghanistan.




The Research Proposal

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Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design

Questionnaire Design

  • Example __closed question

e.g.. do you use the rice variety introduced by the extension agency”

- yes 􀀀 no 􀀀




The Research Proposal

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design

Questionnaire Design

When formulating questions, remember:

  • Clarity:

All notions and concepts should be clearly defined, and ensure that all members of the team and enumerators (if used) have the same understanding of the concept.



The Research Proposal

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design

Questionnaire Design

When formulating questions, remember:

  • Simplicity:

Put questions but in a direct form in a vocabulary that is easy to understand. Use local terminology (measures of area, weight).



The Research Proposal

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design

Questionnaire Design

When formulating questions, remember:

  • Neutrality:

do not ask ’’leading questions’’ - that is questions that suggest or hint that a particular answer is the correct one.




The Research Proposal

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design

Questionnaire Design

When formulating questions, remember:

  • Sensitivity:

consider if your respondents are likely to feel offended or answer honestly.



The Research Proposal

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Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design

Questionnaire Design

Operationalizing the Variables __ Dependent variables

The dependent variables are those that are observed to change in response to the independent variables

what is your total production figure

what is your net profit



The Research Proposal

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design

Questionnaire Design

Operationalizing the Variables __ Independent variables

The independent variables are those that are deliberately manipulated to invoke a change in the dependent variables.

who makes the rules regarding access and distribution of land

who enforces the rules of land in case of a problem



The Research Proposal

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design

Questionnaire Design

Conceptual category, factors and measures used in questionnaire design:

VARIABLE OPERATION MEASURE




The Research Proposal

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design

Questionnaire Design

VARIABLE OPERATION MEASURE

Household Characteristics:

1. Age Age of the small/large-scale miner & farmer at the time

of interview.



The Research Proposal

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design

Questionnaire Design

VARIABLE OPERATION MEASURE

Household Characteristics:

2. Education Number of years of formal school completed.



The Research Proposal

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design

Questionnaire Design

VARIABLE OPERATION MEASURE

Household Characteristics:

3. Household Size Number of people living in the same household.

4. Income An estimate based on market value of gold and farm produce sold or export




The Research Proposal

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design

Questionnaire Design

VARIABLE OPERATION MEASURE

Mine/Farm Characteristics:

5. Mine/Farm size Number of hectares/acres cultivated/mined in

the study year

6. Scale of Technology Number and type of equipment used on farm/mine



The Research Proposal

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Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design

Pre – testing the questionnaire:

Pre- testing questionnaire along an informal survey of the community as part of a familiarization or feasibility study

Final questionnaire based __ feedback from respondents/ informants



The Research Proposal

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design

Units of Analysis:

  • A unit of analysis – the unit to be sampled - can be anything.

--- administrative or social community, a household, a person, a farm, a field, a crop, a herd, an animal, etc.

Example: the main unit of analysis in this study will be small and large scale miners and, heads of peasant and plantation house-holds.




The Research Proposal

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design

Research Assistants:

Help in formal survey

Knowledge of geographic locale; ability to speak/write local dialect

Knowledge about local customs and traditions in the study area

Organize an intensive training programme – interview technique

Participate during the pre-testing of questionnaire



The Research Proposal

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Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design

Code Book:

Appendix 2.1.: Code-book 

  • Survey Questionnaire: Linkage and interaction Interface Between Agriculture and Mining-related Socio-economic Activities



The Research Proposal

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Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design

Code Book:

RESPID 1 RESPONDENT IDENTIFICATION

1001 = Peasant Agriculture

2001 = Plantation Agriculture

3001 = Small-scale gold-mining

4001 = Large-scale gold-mining



The Research Proposal

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Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design

Code Book:

GENDER 3 GENDER OF RESPONDENT

1 = Male

2 = Female




The Research Proposal

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ethnography

…studies cultural patterns and perspectives of participants in their natural settings

ethology

…compares the origins, characteristics, and culture of different societies

ethnomethodology

…studies how people make sense of their everyday activities in order to behave in socially accepted ways

grounded theory

…investigates how inductively-derived theory about phenomenon is grounded in the data of a particular setting

phenomenology

…considers how the experience of particular participants exhibits a unique perspective

symbolic interaction

…investigates how people construct meaning and shared perspectives by interacting with others

action research

…used to improve the practitioner’s practice by doing or changing something

Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design

Qualitative Approaches __ Ethnography

The ethnographic method within qualitative research approach comes largely from the field of anthropology.

The emphasis in ethnography is on studying an entire culture.



The Research Proposal

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Ethnography

Qualitative Research Methods

Ethnography

  • History & Definition

Roots traced back to late 19th century anthropologists who engaged in participant observation in the field.

Derived from the words “ethno” which means folk and “graph” derived from writing.

Ethnography

  • History & Definition

Sociological ethnography is assumed to have originated in the 1920s when a group of Chicago students were instructed to put down their texts and use their eyes and ears

Became known as the Chicago School in the 30s with two strands – 1) concerned w/ the sociology of urban life and the movement between over time, (Park & Burgess’) 2) urban settings including ‘underdog’ occupations and ‘deviant’ roles (Hughes)

Franz Boas

Franz Boas (July 9, 1858 – December 21, 1942) was a German-American anthropologist who has been called the "Father of American Anthropology". Like many such pioneers, he trained in other disciplines; he received his doctorate in physics, and did post-doctoral work in geography. He is famed for applying the scientific method to the study of human cultures and societies.

Franz Boas

In 1883 Boas went to Baffin Island to conduct geographic research on the impact of the physical environment on native Inuit migrations. http://maps.google.com/maps?hl=en&q=Baffin+Island&um=1&ie=UTF-8&sa=X&oi=geocode_result&resnum=1&ct=image

In January, 1887, he was offered a job as assistant editor of the journal Science, in New York.

In 1892 Boas joined a number of other Clark faculty in resigning, to protest Hall's infringement on academic freedom.

Boas was then appointed chief assistant in anthropology at the 1893 World's Columbian Exposition in Chicago.

Boas was appointed lecturer in physical anthropology at Columbia University in 1896, and promoted to professor of anthropology in 1899.

Boas's program at Columbia became the first Ph.D. program in anthropology in America.

Franz Boas

Boas identified two basic questions for anthropologists: "Why are the tribes and nations of the world different, and how have the present differences developed?"

“ We do not discuss the anatomical, physiological, and mental characteristics of man considered as an individual; but we are interested in the diversity of these traits in groups of men found in different geographical areas and in different social classes. It is our task to inquire into the causes that have brought about the observed differentiation, and to investigate the sequence of events that have led to the establishment of the multifarious forms of human life. In other words, we are interested in the anatomical and mental characteristics of men living under the same biological, geographical, and social environment, and as determined by their past.”

Franz Boas

"Franz Boas posing for figure in US Natural History Museum exhibit entitled "Hamats'a coming out of secret room" 1895 or before. Courtesy of National Antropology Archives. (Kwakiutl culture)

At both Columbia and the AAA, Boas encouraged the "four field" concept of anthropology; he personally contributed to physical anthropology, linguistics, archaeology, as well as cultural anthropology.

Potlatch

Celebration of births, rites of passages, weddings, funerals, namings, and honoring of the deceased are some of the many forms the potlatch occurs under. Although protocol differs among the Indigenous nations, the potlatch will usually involve a feast, with music, dance, theatricality and spiritual ceremonies. The most sacred ceremonies are usually observed in the winter.

The potlatch is a festival or ceremony practiced among Indigenous peoples of the Pacific Northwest Coast. At these gatherings a family or hereditary leader hosts guests in their family's house and hold a feast for their guests. The main purpose of the potlatch is the re-distribution and reciprocity of wealth.

Boas & Potlatch

  • Chief O’waxalagalis of the Kwagu'ł describes the potlatch in his famous speech to anthropologist Franz Boas,
  • "We will dance when our laws command us to dance, and we will feast when our hearts desire to feast. Do we ask the white man, 'Do as the Indian does?' It is a strict law that bids us dance. It is a strict law that bids us distribute our property among our friends and neighbors. It is a good law. Let the white man observe his law; we shall observe ours. And now, if you come to forbid us dance, be gone. If not, you will be welcome to us.”
  • Potlatching was made illegal in Canada in 1885 and the United States in the late nineteenth century, largely at the urging of missionaries and government agents who considered it "a worse than useless custom"[citation needed] that was seen as wasteful, unproductive which was not part of "civilized" values.

Ethnography

  • History & Definition

Ethnography: highly descriptive writing about a particular group of people

1. both a process (the research) and a product (the writing)

2. Many forms:

- life history - critical ethnography

- autoethnography - feminist ethnography

Ethnography

  • not a ‘method’ or ‘procedure’ rather a methodological approach: combination of subject matter, epistemology, and practice

ethno [nation]
+ graphy [writing]

ethnography (simplest, clearest definition) – is the study of culture through the researcher’s immersion in that culture for a lengthy period of time.

ethno [nation] + graphy [writing] – writing a nation – does that illuminate the matter a bit? Maybe a little bit.

nation = defn 4 from the OED - “an aggregation of persons of the same ethnic family, often speaking the same language or cognate languages.”

Writing (description and argument) as a critical aspect (not just the transparent recording of findings).

If you are doing naturalistic observation are you doing ethnography? Not necessarily, but some observation and immersion is necessary but not sufficient.

*

Ethnography – characterized by…

subject: the holistic study of people, culture, societies, social relations, social processes, behaviour in situ

method: some component of participant-observation

analysis and writing style: inductive analysis, use of ‘thick description’ and narrative, emic accounts

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Ethnography – characterized by…

thick description

Keeping intact (holism)

‘You are there’ feeling

Not just observing action, understanding symbolic action

[see Geertz, C. (1975). Thick Description: Toward and Interpretive Theory of Culture. In C. Geertz (Ed.), (pp. 3-30). London: Hutchinson, Basic Books.]

[time-use diary from naturalistic
observation + self-observation –
is this ethnography?]

Extensive use of unaltered data

Interpretation and meaning (wink vs. blink)

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Ethnography

  • History & Definition

3. Requires:

- the language of that culture

- first-hand participation & interpretation

- intensive work with a few informants from that setting

Sort of description that can only emerge from spending a lengthy amount of time intimately studying and living in a particular social setting

(Van Maanen, 1982, p. 103-104)

Ethnography

  • History & Definition

4. Must use the lens of culture to understand the phenomenon being examined

5. Must also depict the researchers understanding of the cultural meaning of the phenomenon

Ethnography

  • History & Definition

Culture: the beliefs, values, and attitudes that structure the behavior patterns of a specific group of people

Wolcott (1999) says ethnography “must provide the kind of account of human social activity out of which cultural patterning can be discerned.”

Ethnography

  • History & Definition

Cultural: at a minimum, similarity must be shared by a significant number of members of a social group; shared in the sense of being behaviorally enacted, physically possessed, or internally thought

- intersubjectively shared

- must have potential to be passed on to next generation, to exist with permanency through time and across space (D’Andreade, 1992)

Ethnography

  • History & Definition

Today many non-anthropologists examine subcultures

Subculture: group having social, economic, ethnic, or other traits distinctive enough to distinguish it from others within the same culture or society

Ethnography

  • History & Definition

The heart of ethnography is thick description, that was originally coined by Clifford Geertz (1973).

Thick description: explains not just the behavior, but its context as well, such that the behavior becomes meaningful to an outsider

- analyzes the multiple levels of meaning in any situation

Ethnography

  • History & Definition/Thick description:

Geertz discusses the role of the ethnographer. Broadly, the ethnographer's aim is to observe, record, and analyze a culture. More specifically, he or she must interpret signs to gain their meaning within the culture itself. This interpretation must be based on the "thick description" of a sign in order to see all the possible meanings. His example of a "wink of any eye" clarifies this point. When a man winks, is he merely "rapidly contracting his right eyelid" or is he "practicing a burlesque of a friend faking a wink to deceive a an innocent into thinking conspiracy is in motion"? Ultimately, Geertz hopes that the ethnographer's deeper understanding of the signs will open and/or increase the dialogue among different cultures.

Ethnography

What can be studied:

- Tribes

- Subcultures

- Public realm

- Organizations

- War

Kinds of data:

- Interviews

- Field notes

- Texts

- Visual data

- Transcripts

Ethnography

  • Doing Ethnography

Aims of Observational Research:

Seeing through the eyes of the people being observed

Description: paying attention to the mundane details

Contextualism: conveying messages in a complete manner so that understand the wider social and historical context

Ethnography

  • Doing Ethnography

Aims of Observational Research:

4. Process: viewing social life as involving interlocking series of events

5. Flexible research design: adapting research methods to various situations as they unfold

6. Avoiding early use of theories and concepts

Ethnography

  • Doing Ethnography

4 Separate Sets of Notes Needed:

Short notes made at the time

Expanded notes made as soon as possible after the field session

A fieldwork journal to record problems and ideas that arise during each stage of field work

A provisional running record of analysis and interpretation

(Spradley, 1979)

Ethnography

  • Doing Ethnography

In order to increase reliability creating contact summary sheets is suggested.

Reliability:

Joppe (2000) defines reliability as:

…The extent to which results are consistent over time and an accurate representation of the total population under study is referred to as reliability and if the results of a study can be reproduced under a similar methodology, then the research instrument is considered to be reliable. (p. 1)

Why Important:

Guide planning

Suggests new or revised codes/themes

Coordinates several fieldworkers work

Serves as a reminder of the contact at a later date

Serves as the basis for data analysis

Ethnography

  • Doing Ethnography

In order to increase reliability creating contact summary sheets is suggested.

Validity:

Joppe (2000) provides the following explanation of what validity is:

Validity determines whether the research truly measures that which it was intended to measure or how truthful the research results are. In other words, does the research instrument allow you to hit "the bull’s eye" of your research object? Researchers generally determine validity by asking a series of questions, and will often look for the answers in the research of others. (p. 1)

Triangulation

Triangulation has risen an important methodological issue in naturalistic and qualitative approaches to evaluation [in order to] control bias and establishing valid propositions because traditional scientific techniques are incompatible with this alternate epistemology.

Ethnography

  • Doing Ethnography

Questions for Contact Summary Sheets:

1. What people, events, or situations were involved?

2. What were the main themes or issues in the contact?

3. Which research questions did the contact bear most centrally on?

4. What new hypotheses, speculations, or guesses about the field situations were suggested by the contact?

5. Where should the fieldworker place most energy during the next contact, and what sorts of information should be sought?

Ethnography

  • Methodological Issues

2. Choosing a research site

a. “case the joint”

3. Gaining access

a. 2 types of research settings:

- closed or private

- open or public

Ethnography

  • Methodological Issues

4. Finding an identity

a. Observers may change the situation just by their presence

- Problematic features of fieldwork identity:

1. whether the researcher is known to be a researcher by all of those being studied, or only by some, or by none

Ethnography

  • Methodological Issues

4. Finding an identity/Problematic features of fieldwork identity:

4. what the orientation of the researcher is, and how completely he or she consciously adopts the orientation

of insider or outsider

Ethnography

  • Methodological Issues

4. Finding an identity

b. gender may be an issue that should be reflected upon

5. Looking as well as listening

a. explain the situation as you would to a sighted person who is blindfolded

Ethnography

  • Methodological Issues

6. Recording observations

a. must decide what is the best format to record note in

b. must decide what to “weed out”

- otherwise your data can be overwhelming

c. analyze as you go/catagorize

Ethnography

  • Methodological Issues

7. Developing analysis of field data

a. testing hypotheses/theories generated in the field

- combines insight with rigor

- 5 ways to test emerging hypotheses/theories

1. comparison of different groups at one time and of one cohort w/ another over the course of observation

Ethnography

  • Methodological Issues

7. Developing analysis of field data/5 ways to test emerging hypotheses

2. replication of responses

3. careful revision of negative or deviant cases leading to the abandonment, revision or even reinforcement of the

hypothesis

Ethnography

  • Methodological Issues

7. Developing analysis of field data/5 ways to test emerging hypotheses

4. use of simple tabulations where appropriate

5. provision of sufficient ‘raw’ data

Ethnographic Fieldwork

  • Extended on-location research to gather detailed and in-depth information on a society’s customary ideas, values, and practices through participation in its collective social life.

Fieldwork

  • Ecologist James Kremer and anthropologist Stephen Lansing who have researched the traditional rituals and network of water temples linked to the irrigation management of rice fields on the island of Bali in Indonesia are explaining a computer simulation of this system to the high priest of the supreme water temple, as other temple priests look on.

Participant Observation

  • A research method in which one learns about a group’s beliefs and behaviors through social participation and personal observation within the community, as well as interviews and discussion with individual members of the group over an extended stay in the community.

Key Consultant

  • A member of the society being studied, who provides information that helps researchers understand the meaning of what they observe.
  • Early anthropologists referred to such individuals as informants.

Quantitative Data

  • Statistical or measurable information, such as demographic composition, the types and quantities of crops grown, weapons used, rape cases, or the ratio of spouses born and raised within or outside the community.

Qualitative Data

  • Non statistical information such as personal life stories and customary beliefs and practices.

Photographs

  • Anthropologists sometimes use photographs during fieldwork as eliciting devices, sharing pictures of cultural objects or activities for example, to encourage locals to talk about and explain what they see.

Interviewing

  • Informal interview
  • An unstructured, open-ended conversation in everyday life.
  • Formal interview
  • A structured question/answer session carefully notated as it occurs and based on prepared questions.

Challenges of Anthropology

  • Among the numerous mental challenges anthropologists commonly face are
  • Culture shock
  • Loneliness
  • Feeling like an ignorant outsider
  • Being socially awkward in a new cultural setting.

Challenges of Anthropology

  • Physical challenges typically include:

  • Adjusting to unfamiliar food, climate, and hygiene conditions
  • Needing to be constantly alert because anything that is happening or being said may be significant to one’s research.
  • Ethnographers must spend considerable time interviewing, making copious notes, and analyzing data.

Accurately Describing a Culture

  • To accurately describe a culture an anthropologist needs to seek out and consider three kinds of data:

The people’s own understanding of their culture and the general rules they share.

The extent to which people believe they are observing those rules.

The behavior that can be directly observed.

Feminist Methods of Research

Feminist thinkers have changed

  • Theory about oppression, power, inequality
  • Connection between scholarship and activism
  • Challenged our assumptions about research and the creation of knowledge
  • Questions asked
  • Interpretations
  • Use of research
  • Written text

Feminist Research- Terms

  • Connection of theory and methods
  • Epistemology- theory of knowledge
  • (roles, ethics, interpretation, assumptions)
  • Methodology- theory and analysis of how research should proceed
  • Methods- techniques for gathering data

Historical Legacy of Gender Relations

Restricting Women to the Private Sphere

Justified by three appeals:

  • Protection of women as a class:
  • Moral (♀ as purer; ♀ as children)
  • Physical (health hazards, esp. to justify exclusion from ♂ jobs)
  • Motherhood:

A woman’s place… Essentialist notions of parenting. Emotion work. Public sphere activities threatens the family.

3. Marriage:

Historical reality that single women have possessed more freedom

Change in how we conceptualize research (begins in 1970s)

  • Modern/positivist
  • Measurement, prediction
  • Deduction- general to specific (based on laws, rules
  • Truth,
  • Objectivity of researcher
  • Postmodern/ Post-positivist
  • Experience, description
  • Induction (specific to general)
  • Multiple truths of experience
  • Subjectivity of researcher

Feminist Research

Postmodernism is largely a reaction to the assumed certainty of scientific, or objective, efforts to explain reality.

Postmodernism relies on concrete experience over abstract principles, knowing always that the outcome of one's own experience will necessarily be fallible and relative, rather than certain and universal.

Post-positivism

  • Critique, opposition, and/or rejection of positivisms central tenets. Positivism asserts that the natural and social worlds can be understood through application of scientific method . Inherent in this are a clear set of assumptions about the world and the nature of knowing. But post-positivists reject these assumptions and question just about everything the positivists ‘know’ to be true. For example, post-positivists don't believe the world is know-able or predictable. Rather it is ambiguous, infinitely complex, variable and open to interpretation. And in such a chaotic system, positivist approaches to knowing (i.e. objective, scientific, empirical methods) (see empiricism) are not only seen social ...

Feminist Research

  • Starting point:

  • Neglect of women’s perspectives and experiences in production of knowledge
  • Making women central to research as researchers and as subjects

Feminist Research

  • Ask research questions from a woman’s point of view
  • Challenging the universality of scientific fact or truth

  • Challenging sexism, racism, class, and colonialism in research process
  • Representing diversity

  • Breaking down power imbalance between researcher and researched (self-reflexive)
  • Emancipatory and liberatory goals
  • Social change

Phenomenology

  • Phenomenology is both philosophy and a research method
  • Purpose of this research method is to describe experiences as they are lived – to capture the

“lived experience”

  • Developed by Husserl & Heidegger – an approach to thinking about people’s life experiences.

Phenomenology Cont’d

Philosophical Orientation

view the person as integral with the environment

World is shaped by the self and also shapes the self

The body, the world and the concerns, unique to each person, are the “context” within which that person can be understood

“being in time”

Phenomenology Cont’d

  • A phenomenological researcher asks the question:

“What is the essence of this phenomena as experienced by these people and what does it mean?”

Assumption: there is an “essence”

an essential variant structure

Investigates subjective phenomena

Belief that truths about reality are grounded in peoples’ lived experiences

Phenomenology Cont’d

  • Two Schools of Thought:

Descriptive phenomenology

Interpretive phenomenology (hermeneutics)

Phenomenology Cont’d

  • Four aspects of the lived experience:

SPATIALITY

CORPOREALITY

TEMPORALITY

RELATIONALITY

Phenomenology Cont’d

  • Phenomenologists believe – human existence is “meaningful” and “interesting”
  • “Being in the world” or “Embodiment” is a concept that acknowledges people’s physical ties to their world
  • People:

THINK

SEE

HEAR

FEEL

CONCIOUS OF THEIR BODIES INTERACTION WITH THE WORLD

Phenomenology Cont’d

  • Data sources:
  • In-depth conversations
  • Researcher helps the participant to describe lived experiences without leading the discussion
  • Two or more interviews/conversations are needed
  • Usually small number of participants (ie. 10 or less)
  • May use participation, observation and introspective reflection

SELECTING A PEAK TO CLIMB

UNDERSTANDING THE DECISION MAKING PROCES OF EXPEDITION GROUPS IN THE NEPAL HIMALAYA

Bret Meldrum

Understanding the Decision Making Process

  • Purpose of the Study
  • Description of the Process
  • Lessons learned
  • Applications of the study

Purpose

  • Understanding decision making factors of climbing tourist to Nepal.
  • Describe experience upon a peak to climb.

Description of the process

  • Research
    pre-knowledge
  • Factors for decisions
  • Elevation of mountain
  • Pioneering opportunities
  • Phase 1
  • Collect contact information.
  • Participants selection
  • Coordinate meetings in Katmandu

Description of the process

  • Phase 2:
  • In-depth, face-to-face interview.
  • Capture the content meaning.
  • Semi-structured Interview
  • General frame-setting questions
  • Probing questions
  • Point theoretical saturation
  • Rich Text is generated

Description of the process

  • Phase 3:
  • Cluster themes
  • Considerations coded analyzed
  • Develop models
  • Compare with theoretical literature

Applications of the Study

  • Enhance Conservation Planning
  • Minimize social impacts

Recap __ Phenomenology…as a Methodology

  • …is focused on the subjective experience of individuals or groups.
  • …is personal. The world as experienced by the individual, not relationships between people.
  • …uses small, purposive samples of 3-10 participants that have experienced the phenomenon.
  • …attempts to describe accurately a phenomenon from the person’s perspective.

Phenomenologists….

  • REJECT scientific realism (objects exist independently of our knowledge of their existence).
  • DISAGREE that the empirical sciences are better methods to describe the features of the world.
  • DESCRIBE the ordinary, conscious experience of things.
  • OPPOSE the acceptance of unobservable things.
  • REJECT positivism.

  • BELIEVE objects in the natural world, cultural world, and abstract objects (like numbers and consciousness) can be made evident and thus known.
  • RECOGNIZE the role of description prior to explanation by means of causes, purposes, or grounds.
  • STUDY the “life-world” (the taken-for-granted pattern of everyday living).

http://www.phenomenologycenter.org/phenom.htm#2


Strengths of
phenomenology

  • Efficient and Economical (only in terms of data generation or maybe not at all. . .)
  • Direct Interaction with Participants
  • Allows the researcher to ask for clarification and to ask immediate follow-up/probing questions
  • Allows the researcher to observe nonverbal responses which can be supportive or contradictory to the verbal responses
  • Data is in the participants’ own words

More Strengths

Synergy: participants react to and build upon the responses of other participants.

Flexible research tool

  • Applicable to a wide range of settings and individuals.

Results are easy to understand (in terms of people’s direct opinions and statements)

Marvin Farber 1966

Therefore, it is useful for…

A researcher who wants to understand human experience.

  • A researcher who is willing to become closely entwined

with the research.

Weakness of
phenomenology

  • Findings are difficult to generalize to a larger population
  • Small number of participants who are often attained in a convenient manner
  • Individual responses are not always independent of one another
  • Dominant or opinionated members may overshadow the thoughts of the other group members (only if group interviews are performed).
  • Data is often difficult to analyze and summarize.
  • Researcher may give too much credit to the results (immediacy of a personal opinion)

  • It is a “soft science” at best, really it is not science, it is more like philosophy and religion (Charles Harris, 2006)
  • Critics of phenomenology think you cannot describe the unique experiences AND make generalizations about the experiences at the same time.

Marvin Farber 1966

The Purpose of Action Research

  • Contributes to the theory & knowledge base to enhance practice
  • Supports the professional development of practitioners
  • Builds a collegial networking system

  • Helps practitioners identify problems & seek solutions systematically
  • Can be used at all levels & in all areas of education

Formal Research vs. Action Research

  • Skills needed
  • Goals
  • How the research problem is identified
  • Literature review
  • Selection of participants
  • Research design
  • Data collection
  • Data analysis
  • Application of results

*

It is to be noted that these components exist in both formal research and action research. The goals and the process for identifying the research problem are the two components in action research that vary a bit from “formal research.” In general, they are similar. In action research, though, the goals are more focused on problem solving and the enhancement of professional practice. The goals of “formal research” lean more toward contributing to the body of knowledge in the field, in addition to contributing to the enhancement of practice.

Both “formal research” and Action Research use quantitative and qualitative designs and data collection and analysis strategies. It is important for the Action Researcher to understand both families of research in order to conduct Action Research appropriately, and to understand and be able to analysis and apply the literature reviewed.

Skills Needed

General research skills:

  • Ability to design research
  • Ability to develop instruments
  • Ability to select subjects (if necessary)
  • Ability to collect data
  • Ability to analyze data

*

These skills are needed in both “formal research” and action research.

Goals

Goals…

  • Overall goal should be to solve a problem
  • Include collaboration
  • Professional development
  • Enhance professional practice

*

These goals are specific to action research, but can also be applied as “sub goals” to “formal research.”

Identifying the Problem

First, select a general idea or area of focus:

  • should be within your locus of control
  • should be something you feel passionate about
  • should be something you would like to change or improve

*

This process if specific to action research, but can be helpful in “formal research,” as well.

Identifying the Problem

Second, do Reconnaissance:

  • Explore your understanding of theories, your educational values, how your work fits into the larger context of schooling, the historical context of your school, the history of the development of your ideas about teaching and learning

  • Describe the Who, What, When & Where of the situation you want to change
  • Explain the Why of the situation

*

This is the step of clarifying your area of focus. When it comes to the “Why” of the situation, you will likely be trying out a few hypotheses about the situation. This is where reviewing the literature becomes absolutely critical. This is where you may find potential promising practices that may correct the problem you are addressing.

Proactive Action Research

  • A new practice is tried to bring improved outcomes
  • Hopes & concerns are incorporated
  • Data are collected regularly to track changes
  • Reflection on alternatives takes place
  • Another practice is tried
  • Process begins again

Responsive Action Research

  • Data collected to diagnose situation
  • Data analyzed for themes & ideas
  • Data distributed & changes to be tried announced
  • New practice tried
  • Reactions checked
  • Data collected to diagnose
  • Process begins again

The Process of Action Research

  • Identify the problem; select an area of focus.
  • Review the related research literature.
  • Collect the data.
  • Organize, analyze & interpret the data.
  • Take the action (apply the findings).

Overview

Identify the

problem or area

Review related

research literature

Collect data

Organize, analyze

& interpret

Take action;

apply findings

Identify the Problem
Select the Area of Focus

  • Determine & describe the current situation
  • Discuss
  • Negotiate
  • Explore opportunities
  • Assess possibilities
  • Examine constraints

*

The researcher first assesses the existing situation. Through discussion and negotiation, one can narrow the focus of the research to the salient elements to be studied. Opportunities and resources for data collection and analysis should be examined, as should potential limitations in the environment. The result of these activities should be the concrete identification of what is to be the focus of the action research.

Review the Related Literature

  • Become familiar with other research done on the area of focus
  • Utilize the findings of others to help develop the plan
  • Apply research findings through the lens of others’ experience

*

It’s true that all research requires the foundation of prior research. Research often suggests theory, which can then be tested for its relevance to reality. The more one knows about the area of focus, the more precise will be the action research to be conducted.

Collect the Data

  • Using a variety of data collection strategies, gather information that will contribute to the findings
  • Triangulate
  • Data should be analyzed as it is collected

*

The literature reviewed and the definition of the area of focus should help the researcher determine what data is to be collected. In Action Research, there are always multiple sources of data, multiple kinds of data, and multiple strategies for collecting data (triangulation).

Organize, Analyze & Interpret the Data

  • As the data is collected, it is also continually organized & analyzed

  • As new perspectives are gained on the original area of focus, the problem statement may change

  • Interpretation is based on ongoing analysis & continually reviewing the area of focus

Take Action; Apply Findings

  • Draw conclusions from the data analyzed
  • Translate conclusions into actions or behaviors
  • Plan how to implement the actions or behaviors
  • Do it!

Planning Action Research

Write an area-of-focus statement.

Define the variables.

Develop research questions.

Describe the intervention or innovation.

Describe the action research group.

Describe the negotiations that need to happen.

Develop a timeline.

Develop a statement of resources.

Develop data collection ideas.

Put action plan into action.

*

These are the specific steps that you would take to plan out your action research.

Area-of-Focus Statement

  • Identifies the purpose of the study
  • Identifies the anticipated outcome
  • Identifies the problem to be addressed
  • Completes the statement: “The purpose of this study is…”

Define the Variables

  • Write definitions of exactly what you will address.
  • Definitions should accurately represent what factors, contexts & variables mean to you.
  • Be clear about what is being studied, so that you know it when you see it!

The Research Questions

  • Develop questions that “breathe life” into the area-of-focus statement.
  • Research questions should be open-ended!
  • Research questions help give a focus to the plan.
  • They also help validate that you have a workable plan.

Intervention or Innovation

  • Describe your proposed solution to the initial problem.
  • This is just a statement about what you will do to address the teaching and learning issue you have identified.
  • In “formal research” this would be the experimental treatment.

The Action Research Group

  • Who will you be working with?
  • Why is each member important to the study?
  • What will be the roles & responsibilities of each member?

Negotiations

  • What permissions will you need to secure?
  • Who will be in control of the focus of your study (hopefully, you!)?
  • Who needs to be notified of what?
  • Whose cooperation do you need & how will you get it?

Develop a Timeline

  • This is the essence of planning!
  • Anticipate where & how your study will take place.
  • Anticipate how long each step will take.
  • Apply predicted time frames to a calendar.

Statement of Resources

  • What will you need to carry out your study?
  • Resources include time, money, and materials.
  • Make a list before you get started!

Data Collection Ideas

  • First, decide what kinds of data you will need.
  • Then, determine what kind of access you have to the data.
  • Then, decide how you will gather it.
  • Brainstorm what data naturally occurs in the environment you are studying.

Put the Action Plan into Action

  • From your analysis of the data you collected, you should have elements and ideas you can apply to a plan.
  • Formulate the plans in collaboration with the Action Research Group.
  • Go for it!

Validity of Action Research

  • Validity: the degree to which scientific observations actually measure or record what they purport to measure (Pelto & Pelto, 1978, p. 33)
  • Assessing trustworthiness
  • Assessing understanding

*

Validity in quantitative research refers to accuracy of measurement and ability to generalize results. Of prime consideration in qualitative research is accuracy of measurement. New language – trustworthiness and understanding – is more applicable and appropriate to qualitative research. Since Action Research uses qualitative designs and strategies moreso than quantitative, looking at the validity of Action Research in terms of the trustworthiness of the data and understanding, makes more sense. There are several major theorists whose concepts of validity applied to qualitative and Action Research are important to consider. Our focus will be on Action Research.

Criteria for Assessing Validity

Anderson, Herr & Nihlen:

  • Democratic validity – require accurate representation of multiple perspectives of all subjects
  • Outcome validity – requires that action emerging from a study lead to successful resolution of problem being studied
  • Process validity – requires that study be conducted in dependable & competent way
  • Catalytic validity – requires that subjects are moved to take action
  • Dialogic validity – requires application of a peer review process

*

Applying language like trustworthiness and understanding to the validity of Action Research provides us the opportunity to make sure that our work meets professional standards. Anderson, Herr and Nihlen have offered these criteria as a systematic way to assess the quality of Action Research.

Democratic validity: Make sure “the problems emerge from a particular context and solutions are appropriate to that context” (Cunningham, 1983, p. 30). One way to do so is to involve teachers and administrators in a collaborative effort with subjects. Collaboration is essential to Action Research.

Outcome validity: the study can be considered valid if the results lead to the research learning something that can be applied to the subsequent research cycle.

Process validity: be vigilant in reflecting on the suitability of data collection strategies and modify the strategies if the data is not addressing the research questions.

Catalytic validity: very simply, the results should be a catalyst to taking some action to resolve the original problem.

Dialogic validity: more collaboration! Seeking the input of colleagues and peers establishes how “good” the research is (similar to peer review in traditional publications).

So, ask yourself…

Democratic validity:

Have the perspectives of all of the individuals in the study been accurately represented?

Outcome validity:

Did the action emerging from the study lead to the successful resolution of the problem?

So, ask yourself…

Process validity:

Was the study conducted in a dependable & competent manner?

Catalytic validity:

Were the results of the study a catalyst for action?

Dialogic validity:

Was the study reviewed by peers?

Strategies for Meeting the Criteria

  • Talk Little, Listen a lot!
  • Begin Writing Early!
  • Let Readers “See” for Themselves
  • Report Fully
  • Be Candid
  • Seek Feedback
  • Write Accurately

(Wolcott, 1994)

Ontology - The Nature of “Reality”

Realism

There is an independently existing “objective” reality.

Idealism

“Reality” exists only in our minds

*

Epistemology - The Nature of Knowledge and Meaning

1. Objectivism

Meaning exists in the world.

2. Constructionism

Meaning comes from our interactions with the world (and others).

3. Subjectivism

We impose meaning on the world.

*

Epistemology Personified

Image Sources: http://www.synthstuff.com/mt/archives/bertrand-miner.jpg ; http://www.hmoon.com/garments/cloaks/traveler1-detail.jpg; http://newsimg.bbc.co.uk/media/images/42854000/jpg/_42854125_activist_afp416.jpg

Objectivism

Subjectivism

Constructionism

The Miner

The Traveler

The Activist

Meaning exists in the world

Meaning comes from our interactions with the world

We impose meaning on the world

*

Contrasting the Approaches

Objectivism Construct- ionsim Subjectivism
Goal: Explanation Exploration Empowerment
Looking for: Truth Understanding Progress
Subjectivity: Error Embrace Multiple Perspectives Specific Perspective
Look at: States (static) , Pieces Processes (active), Whole Processes (active), Whole
Design is: Pre-planned Emergent Emergent
Logic: Causal, “hard” Constraining Factors, “Soft” Constraining Factors, “Soft”
Extrapolation: Generalization (given representative sampling) Lessons learned, “petite generalizations” Lessons learned, “petite generalizations”
Kinds of Questions “Does…” “What…” “How…” “How…”

*

But this is a class on research, not philosophy. Why is this epistemology stuff so important?

  • “It is the theory through which we observe a situation that decides what we can observe”

-Albert Einstein

  • “If the only tool you have is a hammer, you tend to see every problem as a nail.”

-Abraham Maslow

  • “Scientists have generated powerful insights by studying light as a wave or a particle. But not as a grapefruit!”

-Gareth Morgan

Image Sources: http://www.mlahanas.de/Physics/Bios/images/AlbertEinstein.jpg; http://www.pedrassoli.psc.br/psicologia/images/maslow.jpg http://www.ebbemunk.dk/technostructure/image_morgan.jpg

(Hockey pucks and music)

*

How Epistemology Drives the Research Process

Epistemology

Research Approach

Topic / Problem

Methodology

Data Collection

Knowledge Claims

Data Analysis

Research Questions

using the logic of

answer

*

Examples of Approaches in Each Epistemology

Objectivism

  • Postivism
  • Post-positivism

Constructionism

  • Phenomenology
  • Hermenuetics
  • Ethnography

Subjectivism

  • Post-modernist
  • Structuralist
  • Post-structuralist
  • Critical Inquiry

*

Some Objectivist Research Approaches

Postivism

  • An independent reality exists and we can learn about it through empirical investigation.

Post-positivism

  • An independent reality exists but we can only know it indirectly. Big focus on the process of inference and warranting knowledge claims.

*

Some Constructionist Research Approaches

Phenomenology

  • The study of lived experience from the perspective of those who experience it (Trying to make meaning of a phenomenon)

Hermenuetics

  • Interpretation of signs and symbols as systems of meanings as they are used (Trying to make meaning of a symbol system)

Ethnography

  • Study of how people in a group manage and organize their lives as social actors (Trying to make meaning of a culture)

*

Some Subjectivist Research Approaches

Post-modernist

  • Research constructs, rather than reveals, meaning. Focus on subjectivity and plurality of meaning across (and within) participants and the researcher.

Structuralist & Post-structuralist

  • The study of how meaning is produced within a culture through its structures (linguistic, psychological, sociological). Post-structuralism does not assume critiques structuralist assumptions that structures are unitary, uncontentious and timeless

Critical Inquiry & Feminist

  • Driven by a concern with existing power inequalities, actively promotes social change, often begins begins with the standpoints and experiences of the less powerful

*

Some things to think about for your project

What do you want to be able to say when you are done?

  • Think about what approach makes sense in terms of the kinds of questions you will ask:

  • “Does…”, “What….” (Objectivist)
  • “How does…” (Constructionist (Subjectivist)
  • “How can…”? (Subjectivist)

*

Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design

Precautions in interviewing:

  • 􀂾 Introduce yourself. Explain to the respondent the purpose of the survey, why or how he/she has been selected for interview.




The Research Proposal

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

*

Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design

Precautions in interviewing:

  • 􀂾 Go through the appropriate local authorities.
  • 􀂾 Know at least some expressions (e.g. simple greetings) of the local language.



The Research Proposal

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

*

Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design

Precautions in interviewing:

  • Seek the logic behind the answers, rather than merely record what is said.
  • Be careful to explain or rephrase questions that are not clear or not understood (e.g. using unfamiliar units of measurement).



The Research Proposal

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

*

Subject matter of lecture: The Research Proposal

Chapter Three

Research Methodology and Design

Reference/Bibliography

Appendices :

survey questionnaire

code – book




The Research Proposal

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

*

  • Assignments __ Applied Policy Analysis Project

Requires students/participants to conduct an original piece of research that is relevant to the field of public policy/development.

__ identify a research question that has relevance to policy makers or managers.

__ develop a research methodology to address the question, analyze appropriate data, and report findings in an accessible, accurate and actionable fashion.

This final project is intended to consolidate students’/participants knowledge and challenge them to think in a clear, creative, and concise manner.



The Research Proposal

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The Role of Statistics in Factual-Based Policy-Making

Introduction

The importance of statistics is captured in the following statements:

“ Why do statistics matter? In simple terms, they are the evidence on which policies are built. They help to identify needs, set goals and monitor progress. Without good statistics, the development process is blind: policy-makers cannot learn from their mistakes and the public cannot hold them accountable ” (World Bank, 2000).



Quantitative Methods


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The Role of Statistics in Factual-Based Policy-Making

Introduction

The importance of statistics is captured in the following statements:

“ Why do statistics matter? In simple terms, they are the evidence on which policies are built. They help to identify needs, set goals and monitor progress. Without good statistics, the development process is blind: policy-makers cannot learn from their mistakes and the public cannot hold them accountable ” (World Bank, 2000).


Quantitative Methods


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The Role of Statistics in Factual-Based Policy-Making

Introduction

The importance of statistics is captured in the following statements:

“ No matter whether you are a politician, farmer, industrialist, businessman, or

economist, you need reliable information upon which to base current policy.... We

live in a world of infinite demands on finite resources. Government must therefore

ask itself where the marginal dollar of expenditure will have maximum impact. In

primary health, should we focus on outreach activities to inform society of the

dangers of malaria... or should we increase expenditure on child immunisation

programmes? In primary education, which is most beneficial to our children, more

books or more teachers? ” (Keith Muhakanizi, Director of Economic Affairs,

Ministry of Finance, Government of Uganda, 1999).



Quantitative Methods


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The Role of Statistics in Factual-Based Policy-Making

Introduction

The importance of statistics is captured in the following statements:

“ Sound data represent a key weapon in the battle against poverty ”

(Tadao Chino, 2002).

“ If you can’t measure it, you can’t manage it ” (Kaplan and Norton, 2000)

Thus, this paper will discuss the importance of statistics in any public

policy-making and suggest the ways to incorporate them into policy-making

activities.



Quantitative Methods


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The Role of Statistics in Factual-Based Policy-Making

What is meant by factual-based policy-making?

Public policy is complex because it is diversed. It ranges from various

areas such as agriculture, health, and the arts to housing. Public policy is

also complex because it is interconnected.

Take the issue of child care, a policy proposal in this area is likely to

impact on labour market policies, gender equality, child welfare and

educational system.



Quantitative Methods


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The Role of Statistics in Factual-Based Policy-Making

What is meant by factual-based policy-making?

Public policy is regarded as a product of government or the administrative

system. It is often not seen by the layman as the expression of a democratic process,

expressed through the political system and democratic institutions. There is an

apparent disconnection. Policy is often seen as something delivered to the public

rather than emanated from the public.

Generally, what we believe in is influenced by the information we possess, the

factual data or the statistics. Statistics relate to more than just hard facts. Statistics

help to turn our strategic goals into something concrete, manageable and

achievable.



Quantitative Methods


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The Role of Statistics in Factual-Based Policy-Making

What is meant by factual-based policy-making?

Statistics may therefore take many forms (qualitative and quantitative);

research, analysis of data, economic and statistical modeling, cost / benefit analysis,

opinions and beliefs, as well as methodologies that are used to gather and

synthesize the information.

The better quality information we have, the better-founded our beliefs and

working models will be, and the better-targeted will be our search for factual data.

The factual data is dynamic, not static. It changes constantly as people’s

understanding develops, as new research produces new results and issues

intrinsically change.



Quantitative Methods


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The Role of Statistics in Factual-Based Policy-Making

What is meant by factual-based policy-making?

Maintaining the factual data requires updating of information which involves

advisors and all key stakeholders. The existing research and analysis should be

reviewed constantly. New research should be developed over time.

Definitions of factual-based policy making are generally cited in the following terms:

The Government expects more of policy makers. More new ideas, more willingness to question inherited ways of doing things, better use of statistics and research in policy-making and better focus on policies that will deliver long term goals (UK Government, 2001).



Quantitative Methods


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The Role of Statistics in Factual-Based Policy-Making

What is meant by factual-based policy-making?

Maintaining the factual data requires updating of information which involves

advisors and all key stakeholders. The existing research and analysis should be

reviewed constantly. New research should be developed over time.

Definitions of factual-based policy making are generally cited in the following

terms:

The Government expects more of policy makers. More new ideas, more willingness to question inherited ways of doing things, better use of statistics and research in policy-making and better focus on policies that will deliver long term goals (UK Government, 2001).



Quantitative Methods


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The Role of Statistics in Factual-Based Policy-Making

What is meant by factual-based policy-making?

Therefore, factual-based policy-making means that policy decisions which are

based on careful and rigorous analysis using sound and transparent data. More

specifically, it may be defined as the use of statistics to:

a. Achieve issue recognition;

b. Inform programme design and policy choice;

c. Forecast the future;

d. Monitor policy implementation;

e. Evaluate policy impact.



Quantitative Methods


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The Role of Statistics in Factual-Based Policy-Making

What is meant by factual-based policy-making?

This distinguishes it from public policy based on more conventional policy

development processes where intuitive appeal, tradition, politics or the extension of

existing practice may set the policy agenda.

In recent years, the international community has increasingly focus on

monitoring and evaluating the areas where statistics should be used in support of

policy-making.

However, it is important to realize that policy outcomes are crucially

affected by the use of statistics and statistical procedures in ‘upstream’ stages of

policy-making, such as issue recognition, programme design, policy choice and

accurate forecasting as well as monitoring and evaluation.


Quantitative Methods

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The Role of Statistics in Factual-Based Policy-Making

What is meant by factual-based policy-making?

Sound and transparent statistics are essential for effective policy-making – a

necessary part of the enabling environment for improving development outcomes.

There are rarely a simple link between statistics and the adoption of a particular

policy. Policy-makers often draw different policy conclusions from the same set of

data, owing to differences in the type of analysis undertaken and / or to differences

in value judgments about policy objectives.



Quantitative Methods


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The Role of Statistics in Factual-Based Policy-Making

What is meant by factual-based policy-making?

While factual-based policy-making maybe understood as a roughly chronological

sequence of activities, the production of statistics can have a less direct, but not

necessarily a less important impact on policy-makers.

Thus, from time to time controversies arise at both international and national level

over statistical series. These debates may focus on methodological issues, such as

the coverage, consistency or accuracy of different sources of data, or they may be

concerned with the appropriateness or inappropriateness of particular statistics to

evaluate specific policy arguments.



Quantitative Methods

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The Role of Statistics in Factual-Based Policy-Making

What is meant by factual-based policy-making?

In either case, such controversies can have a powerful indirect effect on

policy-making, and indeed on electoral outcomes, by focusing public

attention on particular policy issues and raising questions about different

types of data.



Quantitative Methods

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The Role of Statistics in Factual-Based Policy-Making

Why is factual-based policy-making important?

There are two reasons why factual-based policy-making is important. It is the

only way of making public policy decisions which is fully consistent with a democratic

political process characterized by transparency and accountability (Bullock et al.,

2001; PARIS21, 2004; Scott, 2005).



Quantitative Methods


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The Role of Statistics in Factual-Based Policy-Making

Why is factual-based policy-making important?

Transparency is desirable on grounds of equity and efficiency. In a democracy,

citizens have the right to know how and why decisions are taken which affect their

lives. Such knowledge is an essential part of good governance.

Transparency affords protection against decision-making processes being

captured by sectional interests or becoming tainted by corruption. Transparency

provides private firms and households with some assurance when taking rational

personal and business decisions – thereby promoting the efficiency of capital

markets which in turn contributes to faster economic growth.



Quantitative Methods


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The Role of Statistics in Factual-Based Policy-Making

Why is factual-based policy-making important?

Factual-based policy-making enhances the accountability of policy-makers.

A central tenet of democracy is that civil servants should be accountable to the

ministers, and that ministers should be accountable to the public.

Both types of accountability require good data to be effective. The availability of

information to citizens allows them to monitor whether the policy made is effective

or otherwise.

For their part, members of the government hold senior civil servants to

account by requiring factual data to show that programmes are being implemented

successfully as planned.



Quantitative Methods


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The Role of Statistics in Factual-Based Policy-Making

Why is factual-based policy-making important?

Each year, the government spends a considerable sum of money on a range of

services and activities intended to benefit the public. If policies are not well

designed and implemented, public services may be of poor quality.

Those intended to benefit may not do so or a significant portion of society may

be excluded from the benefits, or a policy may be successful in achieving its

objectives but the cost of doing so may not represent value for money (UK

Government, 1999).



Quantitative Methods


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The Role of Statistics in Factual-Based Policy-Making

Why is factual-based policy-making important?

The 1999 Modernising Government white paper, published by the UK

Government noted that Government “must produce policies that really deal with

problems, that are forward-looking and shaped by the evidence rather than a

response to short term pressures; that tackle causes not symptoms”.



Quantitative Methods

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The Role of Statistics in Factual-Based Policy-Making

How has the good statistics helped in policy-making?

Statistics to help identify issues

Good statistics enable us to identify issues such as poverty, inflation, air

pollution, unemployment graduates etc. For example, how do we define poverty

line? Do urban or rural households have high poverty line?



Quantitative Methods


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The Role of Statistics in Factual-Based Policy-Making

How has the good statistics helped in policy-making?

Statistics to help identify issues

In Malaysia, the incidence of absolute poverty has traditionally been determined

by reference to a threshold poverty line income (PLI).

This PLI is based on what is considered to be the minimum consumption

requirements of a household for food, and other non-food items, such as rent, fuel

and power. There is no separate PLI for urban and rural households. The proportion

of all households living below this threshold is the proportion living in poverty

– that is the poverty rate. Poverty rates are available for household categories only:

they are not available for individuals separately.



Quantitative Methods


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The Role of Statistics in Factual-Based Policy-Making

How has the good statistics helped in policy-making?

Statistics to help identify issues

The multi dimensional nature of poverty is not encapsulated in the current

narrow definition of poverty thereby resulting in a lack of understanding of

the poverty issues.

It is essential to review the existing research and to have comprehensive

statistical information in order to embark on new approaches to poverty that will

improve the understanding of the poverty problem (Sulochana Nair, 2001).



Quantitative Methods


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The Role of Statistics in Factual-Based Policy-Making

How has the good statistics helped in policy-making?

Statistics to help identify issues

Addressing graduate unemployment is not a new issue but irrespective of

statistical data accuracy and hence, their reliability, statistics can play havoc with

public perception and the determination of policies and the framing of programs.

“We are now caught in a statistical trap. Do we have 80,000 or 18,000

unemployed graduates?” Former Minister in the Prime Minister’s Department

Datuk Mustapa Mohamed recently insisted that there has been a misunderstanding

and the correct figure is 18,000 (Malaysian Business, 2005).



Quantitative Methods


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The Role of Statistics in Factual-Based Policy-Making

How has the good statistics helped in policy-making?

Statistics to help identify issues

The issue of unemployment among local university graduates has caught the

attention of the nation and making newspaper headlines.

Economically, this is not a trivial issue when the amount of public money poured

into local universities, the man-years spent by our unemployed graduates in

studying for a degree, and the frustration of employers in recruiting graduates to

keep industry running.

The ultimate cost will be the loss of economic competitiveness at a time when it

has become so crucial for our country to be globally competitive.



Quantitative Methods


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The Role of Statistics in Factual-Based Policy-Making

How has the good statistics helped in policy-making?

b. Statistics to inform the design and choice of policy

Once a policy issue is identified, the next step is to undertake some analysis, so

that the extent and nature of the problem can be understood. This understanding

provides the basis for any subsequent policy recommendations.

For instance, quality data and sound statistical analysis supported the design of

Ninth Malaysia Plan. Statistical information is also needed for studying growth

prospects in the industrial, employment, agriculture sector etc.



Quantitative Methods


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The Role of Statistics in Factual-Based Policy-Making

How has the good statistics helped in policy-making?

b. Statistics to inform the design and choice of policy

Various techniques are used to target poverty and vulnerability to determine

cost-effective expenditure priorities from a limited budget. For instance, poverty

maps are produced to rank localities by need by combining population census data

with information on consumption expenditure from household surveys.

However, their validity depends crucially on the accuracy and consistency of the

data sources.



Quantitative Methods


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The Role of Statistics in Factual-Based Policy-Making

How has the good statistics helped in policy-making?

Statistics to forecast the future

Good statistics also help in forecasting the future. For example, Input-Output

tables are use for economic forecasting, meanwhile population census data is

important for projection on population, education, health etc.

On health, forecasting is of paramount importance when a country is afflicted by

the outbreak of a serious disease. The government needs to know how quickly it is

likely to spread among the population in order to design appropriate counter-

measures. Having access to accurate data on disease prevalence in the early stages

of an epidemic is crucial to obtaining reliable forecasts of future prevalence.



Quantitative Methods


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The Role of Statistics in Factual-Based Policy-Making

How has the good statistics helped in policy-making?

d. Statistics to monitor policy implementation

Good statistics help to monitor policy implementation. For example, a wide

range of statistical information is used to monitor whether or not we have achieved

the Millennium Development Goals (MDG). However, the lack of baseline data or

information on trends is a serious impediment to implementing a target-driven

development strategy (UNDP, 2005).



Quantitative Methods


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The Role of Statistics in Factual-Based Policy-Making

How has the good statistics helped in policy-making?

Statistics to evaluate policy impact

Evaluating policy impact is more methodologically demanding than monitoring

policy implementation. Statisticians should be involved in the policy making

process at an early stage to advise on how the impact of a new policy will be

assessed (United Nations Statistics Division, 1994). In some instances, this

assessment may need to be undertaken at regular intervals over many years.



Quantitative Methods


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The Role of Statistics in Factual-Based Policy-Making

How has the good statistics helped in policy-making?

Statistics to evaluate policy impact

For instance, in the case of growing demand for imported foods in Malaysia, it

has increased by RM13 billion. It was forecasted that there would be an increase in

demand of foods for RM20 billion in the future. Therefore, there is a need to review

certain parts of the agriculture policy.

Good statistics, therefore, represent a key role in good policy making. The

impact of policy can be measured with good statistics. If policy cannot be measured,

it is not a good policy.



Quantitative Methods


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The Role of Statistics in Factual-Based Policy-Making

How has the good statistics helped in policy-making?

Statistics to evaluate policy impact

The litmus test of good statistics is their quality and accessibility and the

efficiency with which they are produced. Good official statistics must have many

characteristics.

Most basically, official statistics are good only to the extent that they meet the

needs of users. Official statistics must be available to a broad range of public and

private users and be trusted to be objective and reliable. Good statistics must also

have a breadth and depth of coverage to meet all policy needs and to inform the

public so they can evaluate the effectiveness of government actions.



Quantitative Methods

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The Role of Statistics in Factual-Based Policy-Making

How has the good statistics helped in policy-making?

Statistics to evaluate policy impact

There is no doubt that official statistics provide an indispensable element in the

information system of a democratic society, serving the Government, the economy and the

public with data about the economic, demographic, social and environmental situation.

To this end, official statistics that meet the test of practical utility are to be compiled and

made available on an impartial basis by official statistical agencies to honour citizens’

entitlement to public information.


This means that statistics are the backbone and should be relevant for society,

compiled in an impartial manner, be free from political interference and be

accessible for everyone under equal conditions.



Quantitative Methods


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The Role of Statistics in Factual-Based Policy-Making

Where do we get good statistics?

In the Malaysian Statistical System, which is considered to be largely centralized, the Chief

Statistician of the Department of Statistics (DOS) is the head of the national statistical service.

All censuses and surveys conducted on a nationwide basis are the responsibility of DOS.

Other government ministries and departments may have their own statistical and research

units but the statistics they collect and compile are mostly byproducts of the administrative

processes.

In Malaysia, two important committees, namely the Statistics Steering Committee and the

Main Users Committee, were established in March 1988 with the view to determine guidelines,

policy and priorities in statistical activities. The terms of reference of the Statistics Steering

Committee are:



Quantitative Methods


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The Role of Statistics in Factual-Based Policy-Making

Where do we get good statistics?

a. to determine guidelines and policy of DOS;

to coordinate activities related to the collection and dissemination of statistics

carried out by Government agencies; and

c. to provide guidelines towards an effective national information system.



Quantitative Methods


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The Role of Statistics in Factual-Based Policy-Making

Where do we get good statistics?

The Main Users Committee coordinates at the technical level the collection and

dissemination of statistics by Government agencies in order to ensure the efficient

and effective utilization of available resources;

the use of standard concepts,

definitions and classifications;

the use of appropriate and effective methods in the collection and production of

statistics;

the minimization of duplication; and

the production of quality and timely data.


Quantitative Methods


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The Role of Statistics in Factual-Based Policy-Making

Where do we get good statistics?

It was suggested that all the agencies under the two committees to become one

identity known as National Statistical System (NSS). NSS is where data producers,

compilers, analysts and users come under one roof. NSS is important for effective

coordination of the activities of various statistical entities.

The following are some of the objectives which would be difficult to achieve in

the absence of effective coordination within the statistical service:



Quantitative Methods


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The Role of Statistics in Factual-Based Policy-Making

Where do we get good statistics?

a. To ensure a maximum of integration in the statistical process and in its outputs;

To realize the full potential of personnel and other resources in providing quality services;

To promote the use of appropriate and effective methods in the collection and production of statistics;

To apply common standards and best methodology;

e. To identify and define statistical priorities and requirements;



Quantitative Methods

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The Role of Statistics in Factual-Based Policy-Making

Where do we get good statistics?

f. To meet the demand for statistics effectively and efficiently;

To ensure maximum cooperation of data providers;

h. To improve awareness of the importance of statistics;



Quantitative Methods

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The Role of Statistics in Factual-Based Policy-Making

Where do we get good statistics?

Careful decisions need to be taken on how best to develop statistics effectively

and efficiently; and reform is often required across the whole system.

This can be facilitated through the design and implementation of strategic

statistical plans, which are integrated within national policy processes and cover all

data sectors and users.

These National Strategies for the Development of Statistics (NSDSs) provide a

robust framework and action plan for building the statistical capacity to meet both

current and future data needs.


Quantitative Methods


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The Role of Statistics in Factual-Based Policy-Making

Where do we get good statistics?

A NSDSs should:

a. Be nationally led and owned, with high level political support;

Be demand-focused and integrated within national development policy

processes;

Be developed in an inclusive and consultative way;

d. Assess all statistical sectors and user needs and provide a vision and strategic plan for national statistics;



Quantitative Methods


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The Role of Statistics in Factual-Based Policy-Making

Where do we get good statistics?

A NSDSs should:

Set out an integrated statistical development programme to build capacity to

deliver results, which is prioritized and timetabled, incorporating plans for

implementation, monitoring and evaluation but is also flexible enough to cope with

change;

Address institutional and organisational constraints and process, including

resources, for the sustainable development of statistical systems and outputs;

Build quality “fit for purpose”, drawing on best international practice and

standards;



Quantitative Methods


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The Role of Statistics in Factual-Based Policy-Making

Conclusion

Governments, society generally and, the international system need good

statistics. Proper use of good statistics leads to better policy and development

outcomes. Factual-based approaches have a key part to play at two points.

Firstly, there is a growing public demand for analysis before policy is

implemented. Without sound analysis, research and modeling, policy development

will be in the dark and unaware of the impact that the policies are facing.

In addition, the general public want to see the facts and figures and the bodies

that were consulted in respect of new policy initiatives or legislative proposals.


Quantitative Methods

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The Role of Statistics in Factual-Based Policy-Making

Conclusion

Factual-based approaches support transparency and comprehensiveness.

Secondly, in whatever form our initiatives are, for example, grant schemes

or regulations, we need to ensure they are systematically reviewed to check that

they are achieving their desired outcomes. In this case, factual-based approaches

help to ensure effectiveness and efficiency.



Quantitative Methods


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The Role of Statistics in Factual-Based Policy-Making

Conclusion

Making the transition to factual-based policy-making can best be achieved

through formulating a national strategy for the development of statistics, which is

fully integrated into the system of national policy making.

The added value of an NSDS is that it provides strategic planning and priority

setting within the context of the entire statistical system, covering all data sectors

and users as well as essential organisational and institutional issues.



Quantitative Methods


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The Role of Statistics in Factual-Based Policy-Making

Conclusion

Good statistics provide a basis for good decision making, help governments to

identify the best courses of action in addressing complex problems, are essential to

manage the effective delivery of basic services, and are an indispensable, core

requirement for accountability and transparency.

Good statistics are a core component of good governance. They also provide a

sound basis for the design, management, monitoring, and evaluation of national

policy frameworks such as Poverty Reduction Strategies (PRSs) and for monitoring

progress towards the Millennium Development Goals (MDGs).


Quantitative Methods


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The Role of Statistics in Factual-Based Policy-Making

Conclusion

Good statistics, therefore, are part of the enabling environment for

development: they measure inputs, outputs, outcomes, and impact, providing

reliable assessments of key economic and social indicators, covering all aspects of

development from measures of economic output and price inflation, to the well-

being of individuals.

Good statistics, therefore, represent a key role in good policy making. The impact of policy can be measured with good statistics. If policy cannot be measured, it is not a good policy.



Quantitative Methods


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Longitudinal and Cross-sectional Research

Longitudinal research:

The development of longitudinal studies over the last decade has underpinned advances in social science method and in understanding of major social changes and policy interventions. Three different types of longitudinal research methods -- trend, cohort, and panel studies -- are defined and relative advantages and disadvantages are discussed.

These methods provide an understanding of social change, of the trajectories of individual life histories and of the dynamic processes that underlie social and economic life. Their fundamental role in social science and policy research is the core rationale for the continued investment for instance in longitudinal studies.



Quantitative Methods


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Longitudinal and Cross-sectional Research

Longitudinal research:

In common with most societies, sub-Sahara Africa is undergoing considerable socio-economic change. We have an ageing population, increasing diversity of ethnic background and rising levels of instability in both working careers and family life.

Longitudinal studies collect data about different times in individuals’ lives, and across generations, linking evidence from different points in the lives of parents and children.

This capacity to follow individuals through time and observe how experiences and behaviour are influenced by the wider social and economic contexts in which they find themselves – and perhaps how they in turn influence those contexts – gives longitudinal studies a major role in understanding social change.



Quantitative Methods

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Longitudinal and Cross-sectional Research

Longitudinal research:

Longitudinal research can address issues and support methods in ways that are not possible with traditional cross-sectional approaches. It is particularly valuable in a number of research areas:

when the focus is directly on change and the phenomena are themselves inherently longitudinal – for example, the dynamics of poverty, employment instability, social mobility and changing social attitudes;

when investigating causal processes – for example, the effects of unemployment on mental health or of child poverty on later life chances;



Quantitative Methods

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Longitudinal and Cross-sectional Research

Longitudinal research:

when studying social change and needing to separate out age, period and cohort effects;

and when establishing the effect of a ‘treatment’ by following an experimental or quasi-experimental design or comparing periods before and after the introduction of public policy.


PSMTP: Applied Research and Quantitative Methods
Session 8: Explanation

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Longitudinal and Cross-sectional Research

Longitudinal research:

These advantages have been exploited for a wide range of important research findings. For example:

  • Disentangling the effects on children of school and family background in order to understand social mobility and the effectiveness of educational interventions – and to identify the key points for intervention.

  • Examining the effects of changing patterns of marriage, cohabitation and childbirth on the time children are likely to spend in lone parent families – and the effects on their later lives.

  • Understanding the defining characteristics of people who experience repeated spells of unemployment and poverty – and their ‘scarring’ effects, which make it difficult for people to find work and/or escape poverty in the future.


Quantitative Methods


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Longitudinal and Cross-sectional Research

Longitudinal research __trend studies:

The trend study is probably the most common longitudinal study among others. A trend study samples different groups of people at different points in time from the same population.

For example, trend studies are common around public opinion poll. Suppose that 2 months before a year-long gun control campaign, a sample of adults is drawn: 64% report that they're in favour of a strict gun control regulation and 34% report that they are not.

A year later, a different sample drawn from the same population shows a change: 75% report that they're in favour of gun control and 25% report that they are not.  This is a sample example of trend study.



Quantitative Methods

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Longitudinal and Cross-sectional Research

Longitudinal research __trend studies:

Characteristics

Data is collected from the population at more than one point in time. (This does not always mean that the same subjects are used to collect data at more than one point in time, but that the subjects are selected from the population for data at more than one point in time).

In analyzing the data, the investigator draws conclusions and may attempt to find correlations between variables. Therefore, trend studies are uniquely appropriate for assessing change over time and for situation relating (prediction) questions



Quantitative Methods


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Longitudinal and Cross-sectional Research

Longitudinal research __trend studies:

Trend studies are valuable in describing long-term changes in a population. They can establish a pattern over time to detect shifts and changes in some event. Marketing companies, for example, compile trend studies that chart fluctuations in consumption levels for a certain product. Among others there are two important advantages of trend studies.

  • Flexibility

One advantage of trend study is that they can be based on a comparison of survey data originally constructed for other purposes. Of course in utilizing such secondary data, the research needs to recognize any differences in question wording, contexts, sampling, or analysis techniques that might differ from one survey to the next.

  • Cost effectiveness

Since trend studies allow researchers to use secondary data, it saves time, money, and personnel.



Quantitative Methods


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Longitudinal and Cross-sectional Research

Longitudinal research __trend studies:

Trend analysis is to provide descriptive trends of some topic in a certain period of time. Therefore, there is less concerns on internal validity because it does not aim to provide causal inferences as in the case of experimental studies or some penal studies.

Changes in the way indexes are constructed or the way questions are asked will produce results that are not comparable over time.



Quantitative Methods


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Longitudinal and Cross-sectional Research

Longitudinal research __cohort studies:

A Cohort Study is a study in which subjects who presently have a certain condition and/or receive a particular treatment are followed over time and compared with another group who are not affected by the condition under investigation.

For research purposes, a cohort is any group of individuals who are linked in some way or who have experienced the same significant life event within a given period. There are many kinds of cohorts, including birth (for example, all those who born between 1970 and 1975) disease, education, employment, family formation, etc.

Any study in which there are measures of some characteristic of one or more cohorts at two or more points in time is cohort analysis.


Quantitative Methods

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Quantitative Methods


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Longitudinal and Cross-sectional Research

Longitudinal research __cohort studies:

In general, Cohort analysis attempts to identify cohorts effects: Are changes in the dependent variable (health problems in this example) due to aging, or are they present because the sample members belongs to the same cohort (smoking vs. non smoking)? In other words, cohort studies are about the life histories of sections of populations and the individuals who comprise them.

They can tell us what circumstances in early life are associated with the population's characteristics in later life - what encourages the development in particular directions and what appears to impede it. We can study such developmental changes across any stage of life in any life domain: education, employment, housing, family formation, citizenship and health



Quantitative Methods


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Longitudinal and Cross-sectional Research

Longitudinal research __cohort studies:

Advantages

Cohort analysis is an appealing and useful technique because it is highly flexible. It provides insight into the effects of maturation and social, cultural, and political change. In addition, it can be used with either original data or secondary data. In some instances, a cohort analysis can be less expensive than experiments or surveys.



Quantitative Methods


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Longitudinal and Cross-sectional Research

Longitudinal research __cohort studies:

Disadvantages

One of the most difficult tasks in cohort studies is to assess whether associations between cohort and dependent variables derived from the studies are of a causal nature or not. Cohort studies are subject to the influence of factors over which the investigators most often do not have full control, and that findings from these studies are more open to threats to validity than those of studies with an experimental research design.

Because of the lack of randominization in the cohort design, the two groups may differ in ways other than in the variable under study. For example, if the subjects who smoke tend to have less money than the non-smokers, and thus have less access to health care, that would exaggerate the difference between the two groups.



Quantitative Methods


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Longitudinal and Cross-sectional Research

Longitudinal research __cohort studies:

Disadvantages

The other problem with cohort studies is that they can end up taking a very long time, since the researchers have to wait for the conditions of interest to develop.

People die, move away, or develop other conditions, new and promising treatments arise, and so on. If the remaining cohort members differ in regard to the variable under the study, the variation in the cohort study may simply reflect this change.

It is therefore imperative that findings from cohort studies are critically scrutinized before any judgement of causality is made.



Quantitative Methods


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Longitudinal and Cross-sectional Research

Longitudinal research __ panel studies:

Panel studies measure the same sample of respondents at different points in time.

Panel studies can reveal shifting attitudes and patterns of behaviour that might go unnoticed with other research approaches. Depending on the purpose of the study, researchers can use either a continuous panel, consisting of members who report specific attitudes or behaviour patterns on a regular basis, or an interval panel, whose members agree to complete a certain number of measurement instruments only when the information is needed.

In general, panel studies provide data suitable for sophisticated statistical analysis and might enable researcher to predict cause-effect relationships.



Quantitative Methods


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Longitudinal and Cross-sectional Research

Longitudinal research __ panel studies:

Panel data are particularly useful in predicting long-term or cumulative effects which are normally hard to analyze in a one-shot case study (or cross-sectional study).

For example, in the early 80s', the National Broadcasting Company supported a panel study in order to investigate the causal influence of violent TV viewing on aggression among young people.

In brief, the methodology in the study involved collecting data on aggression, TV viewing, and a host of sociological variables from children in several metropolitan cities in the US. About 1,200 boys participated in the study and the variables were measured six times for 3 year study period. The researchers sought to determine whether TV viewing at an earlier time added to the predictability of aggression at a later time. After looking at all the results of data analyses, the investigators concluded that there was no consistent statistically significant relationship between watching violent TV programs and later acts of aggression.



Quantitative Methods


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Longitudinal and Cross-sectional Research

Longitudinal research __ panel studies:

Advantages

Panel data are particularly useful in answering questions about the dynamics of change.

For example, under what conditions do voters change political party affiliation? What are the respective roles of mass media and friends in changing political attitudes? Additionally, as mentioned above, panel study is useful in predicting long-term or cumulative effects which are normally hard to analyze in a one-shot case study (or cross-sectional study).

Finally, panel studies help solve the problems normally encountered when defining a theory on the basis of a one-shot study. Since the research progresses over a period time, the research can allow for the influences of competing stimuli on the subject, which might increase validity of the study.



Quantitative Methods


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Longitudinal and Cross-sectional Research

Cross -sectional research :

A cross-sectional study is one that takes place at a single point in time. In effect, we are taking a 'slice' or cross-section of whatever it is we're observing or measuring. On the other hand, a longitudinal study is one that takes place over time -- we have at least two (and often more) waves of measurement in a longitudinal design.

In cross-sectional studies, the researcher gathers the data at once and then classifies them simultaneous __ categories.

For instance, a researcher wants to know the relationship between mathematics performance and the number of hours spent watching television a day. He/she gathers information on the average number of hours spent watching TV and the performance of the qualifying mathematics examination. Because he/she gathers data at once and classifies the students based on the number of hours (more than 2 classifications in the cross-sectional study) and the math performance, it is the cross-sectional design.



Quantitative Methods


*

Longitudinal and Cross-sectional Research

Cross -sectional research:

Time Pass Fail

Less than l hour a day (Group 1) 20 2

2 hours a day (Group 2) 39 5

3 hours a day (Group 3) 46 7

4 hours a day (Group 4) 57 9

More than 5 hours a day (Group 5) 33 10

In this case, four odds ratios are calculated. If we regard Group 1 as a baseline group, we can calculate the odds ratios of four groups relative to the baseline group (Group 1). The interpretation is "the odds of success(yes, pass, etc.) in Row 1 (Comparison Group) is 0.** times the odds of success in Row 2 (Baseline group)." What odds ratio calculates is the success rate in Row 1 compared to Row 2 group.


Quantitative Methods


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Longitudinal and Cross-sectional Research

Cross -sectional research:

Let's calculate the odds ratio when Group 1 is the baseline. I use the cross-product ratio which is convenient way to calculate the odds ratio.

  • Cross-product ratio, is defined to be

Odds Ratio (calculated by the cross-product ratio) = (n11*n22) / (n12*n21).

Example, the odds ratio calculated by the cross-product ratio is (55*3)/(32*10)=0.516. The cross-product ratio is equivalent to the ratio of two odds (OR=odds1/odds2).



Quantitative Methods

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Longitudinal and Cross-sectional Research

Cross -sectional research:

Example, the odds ratio calculated by the cross-product ratio is (55*3)/(32*10)=0.516. The cross-product ratio is equivalent to the ratio of two odds (OR=odds1/odds2).

Pass Fail

Non-tutoring (CAI) 55 = n11 10 = n12

Tutorin g 32 = n21 3 = n22

It is concluded that the odds of passing the exam in CAI is almost 0.52 times the odds of passing the exam in Tutoring group. In other words, the odds of passing the exam in Tutoring group is almost twice as high as CAI group. Tutoring seems to be better way to improve the math performance in the Remedial Mathematic Program. Because of convenience, the cross-product odds ratio method is preferred in hand calculation.



Quantitative Methods


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Longitudinal and Cross-sectional Research

Cross -sectional research:

The odds ratio of Group 1 and Group 2 is (39*2)/(20*5)=0.78. It means that the odds of passing the exam in Group 2 is 0.78 times the odds of passing the exam in Group 1.

The odds ratio of Group 1 and Group 3 is (46*2)/(20*7)=0.66. It means that the odds of passing the exam in Group 3 is 0.66 times the odds of passing the exam in Group 1.

The odds ratio of Group 1 and Group 4 is (57*2)/(20*9)=0.63. It means that the odds of passing the exam in Group 4 is 0.63 times the odds of passing the exam in Group 1

The odds ratio of Group 1 and Group 5 is (33*2)/(20*10)=0.33. It means that the odds of passing the exam in Group 5 is 0.33 times the odds of passing the exam in Group 1.



Quantitative Methods


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Longitudinal and Cross-sectional Research

Cross -sectional research:

It is concluded that the odds of passing the exam is decreased as the number of hours spent watching TV is increased. If you decide to use Group 5 as the baseline, all other groups are compared with Group 5.



Quantitative Methods


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Sampling

Purposive Sampling

In purposive sampling, we sample with a purpose in mind. We usually would have one or more specific predefined groups we are seeking.

For instance, have you ever run into people in a mall or on the street who are carrying a clipboard and who are stopping various people and asking if they could interview them? Most likely they are conducting a purposive sample (and most likely they are engaged in market research).


Quantitative Methods


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Sampling

Purposive Sampling

Purposive sampling can be very useful for situations where you need to reach a targeted sample quickly and where sampling for proportionality is not the primary concern.

With a purposive sample, you are likely to get the opinions of your target population, but you are also likely to overweight subgroups in your population that are more readily accessible.



Quantitative Methods


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Sampling

Quota Sampling

In quota sampling, you select people non randomly according to some fixed quota. There are two types of quota sampling: proportional and non proportional.



Quantitative Methods


*

Sampling

proportional quota sampling

In proportional quota sampling you want to represent the major characteristics of the population by sampling a proportional amount of each.

For instance, if you know the population has 40% women and 60% men, and that you want a total sample size of 100, you will continue sampling until you get those percentages and then you will stop.

So, if you've already got the 40 women for your sample, but not the sixty men, you will continue to sample men but even if legitimate women respondents come along, you will not sample them because you have already "met your quota."



Quantitative Methods


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Sampling

proportional quota sampling

The problem here (as in much purposive sampling) is that you have to decide the specific characteristics on which you will base the quota. Will it be by gender, age, education race, religion, etc.



Quantitative Methods


*

Sampling

non proportional quota sampling

Non proportional quota sampling is a bit less restrictive. In this method, you specify the minimum number of sampled units you want in each category. Here, you're not concerned with having numbers that match the proportions in the population.

Instead, you simply want to have enough to assure that you will be able to talk about even small groups in the population. This method is the non probabilistic analogue of stratified random sampling in that it is typically used to assure that smaller groups are adequately represented in your sample.



Quantitative Methods


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Sampling

Snowball sampling

In snowball sampling, you begin by identifying someone who meets the criteria for inclusion in your study. You then ask them to recommend others who they may know who also meet the criteria. Although this method would hardly lead to representative samples, there are times when it may be the best method available.

Snowball sampling is especially useful when you are trying to reach populations that are inaccessible or hard to find. For instance, if you are studying the homeless, you are not likely to be able to find good lists of homeless people within a specific geographical area. However, if you go to that area and identify one or two, you may find that they know very well who the other homeless people in their vicinity are and how you can find them.



Quantitative Methods


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Sampling

Expert Sampling

Expert sampling involves the assembling of a sample of persons with known or demonstrable experience and expertise in some area. Often, we convene such a sample under the auspices of a "panel of experts." There are actually two reasons you might do expert sampling.

First, because it would be the best way to elicit the views of persons who have specific expertise. In this case, expert sampling is essentially just a specific sub case of purposive sampling. But the other reason you might use expert sampling is to provide evidence for the validity of another sampling approach you've chosen.



Quantitative Methods


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Sampling

Heterogeneity Sampling

We sample for heterogeneity when we want to include all opinions or views, and we aren't concerned about representing these views proportionately. Another term for this is sampling for diversity.

In many brainstorming or nominal group processes (including concept mapping), we would use some form of heterogeneity sampling because our primary interest is in getting broad spectrum of ideas, not identifying the "average" or "modal instance" ones.



Quantitative Methods


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Time Series

“The Art of Forecasting”

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1

Learning Objectives

  • Describe what forecasting is
  • Explain time series & its components
  • Smooth a data series
  • Moving average
  • Exponential smoothing
  • Forecast using trend models Simple Linear Regression Multiple Regression model

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2

As a result of this class, you will be able to...

What Is Forecasting?

  • Process of predicting a future event
  • Underlying basis of
    all business/policy decisions
  • Production
  • Inventory
  • Personnel
  • Facilities

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4

Types of Forecasting Methods

Forecasting methods are classified into two groups:

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Types of Forecasting Models

Qualitative methods – judgmental methods

  • Forecasts generated subjectively by the forecaster
  • Educated guesses

Quantitative methods – based on mathematical modeling:

  • Forecasts generated through mathematical modeling

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Qualitative Methods

Sheet1

Type Characteristics Strengths Weaknesses
Executive opinion A group of managers meet & come up with a forecast Good for strategic or new-product forecasting One person's opinion can dominate the forecast
Market research Uses surveys & interviews to identify customer preferences Good determinant of customer preferences It can be difficult to develop a good questionnaire
Delphi method Seeks to develop a consensus among a group of experts Excellent for forecasting long-term product demand, technological changes, and scientific advances Time consuming to develop

Sheet2

Sheet3

*

  • Used when situation is ‘stable’ & historical data exist
  • Existing products
  • Current technology
  • Involve mathematical techniques
  • e.g., forecasting sales of color televisions

Quantitative Methods

Forecasting Approaches

  • Used when situation is vague & little data exist
  • New products
  • New technology
  • Involve intuition, experience
  • e.g., forecasting sales on Internet

Qualitative Methods

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6

Causal

Models

Quantitative Forecasting Methods

Quantitative

Forecasting

Time Series

Models

Regression

Exponential

Smoothing

Trend

Models

Moving

Average

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15

What is a Time Series?

Set of evenly spaced numerical data

  • Obtained by observing response variable at regular time periods

Forecast based only on past values

  • Assumes that factors influencing past, present, & future will continue

  • Example
  • Year: 1995 1996 1997 1998 1999

Sales/Outcome: 78.7 63.5 89.7 93.2 92.1

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16

Time Series vs.
Cross Sectional Data

Time series data is a sequence of observations

  • collected from a process
  • with equally spaced periods of time.

Time Series vs.
Cross Sectional Data

Contrary to restrictions placed on cross-sectional data, the major purpose of forecasting with time series is to extrapolate beyond the range of the explanatory variables.

Time Series vs.
Cross Sectional Data

Time series is dynamic, it does change over time.

Time Series vs.
Cross Sectional Data

Presentation: When working with time series data, it is paramount that the data is plotted so the researcher can view the data.

Time Series Components

Trend

Seasonal

Cyclical

Irregular

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21

Trend Component

Persistent, overall upward or downward pattern

Due to population, technology etc.

Several years duration

Mo., Qtr., Yr.

Response

*

22

Trend Component

  • Overall Upward or Downward Movement
  • Data Taken Over a Period of Years

Sales

Time

Upward trend

*

Cyclical Component

Repeating up & down movements

Due to interactions of factors influencing economy

Usually 2-10 years duration

Mo., Qtr., Yr.

Response

Cycle

*

23

Cyclical Component

Upward or Downward Swings

May Vary in Length

Usually Lasts 2 - 10 Years

Sales

Time

Cycle

*

Seasonal Component

Regular pattern of up & down fluctuations

Due to weather, customs etc.

Occurs within one year

Mo., Qtr.

Response

Summer

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24

Seasonal Component

Upward or Downward Swings

Regular Patterns

Observed Within One Year

Sales

Time (Monthly or Quarterly)

Winter

*

Irregular Component

Erratic, unsystematic, ‘residual’ fluctuations

Due to random variation or unforeseen events

  • Union strike
  • War

Short duration & non-repeating

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25

Random or Irregular Component

Erratic, Nonsystematic, Random, ‘Residual’ Fluctuations

Due to Random Variations of

  • Nature
  • Accidents

Short Duration and Non-repeating

*

Time Series Patterns

*

Time Series Forecasting

*

33

Time Series Forecasting

*

35

Time Series Models

  • Naive:
  • The forecast is equal to the actual value observed during the last period – good for level patterns

  • Simple Mean:
  • The average of all available data - good for level patterns

  • Moving Average:
  • The average value over a set time period

(e.g.: the last four weeks)

  • Each new forecast drops the oldest data point & adds a new observation
  • More responsive to a trend but still lags behind actual data

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Time Series Models con’t

  • Weighted Moving Average:

  • All weights must add to 100% or 1.00

e.g. Ct .5, Ct-1 .3, Ct-2 .2 (weights add to 1.0)

  • Allows emphasizing one period over others; above indicates more weight on recent data (Ct=.5)
  • Differs from the simple moving average that weighs all periods equally - more responsive to trends

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Time Series Models con’t

  • Exponential Smoothing:

Most frequently used time series method because of ease of use and minimal amount of data needed

  • Need just three pieces of data to start:
  • Last period’s forecast (Ft)
  • Last periods actual value (At)
  • Select value of smoothing coefficient, ,between 0 and 1.0
  • If no last period forecast is available, average the last few periods or use naive method
  • Higher values (e.g. .7 or .8) may place too much weight on last period’s random variation

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Moving Average Method

Series of arithmetic means

Used only for smoothing

  • Provides overall impression of data over time

Used for elementary forecasting

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37

Moving Average Graph

Year

Sales

Actual

*

53

Moving Average
[An Example]

You work for Firestone Tire. You want to smooth random fluctuations using a 3-period moving average.
1995 20,000
1996 24,000
1997 22,000
1998 26,000
1999 25,000

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54

Moving Average
[Solution]

Year Sales MA(3) in 1,000

1995 20,000 NA

1996 24,000 (20+24+22)/3 = 22

1997 22,000 (24+22+26)/3 = 24

1998 26,000 (22+26+25)/3 = 24

1999 25,000 NA

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55

Moving Average

Year Response Moving Ave

1994 2 NA

1995 5 3

1996 2 3

1997 2 3.67

1998 7 5

1999 6 NA

94 95 96 97 98 99

8

6

4

2

0

Sales

*

Time Series Forecasting

*

57

Exponential Smoothing Method

  • Form of weighted moving average
  • Weights decline exponentially
  • Most recent data weighted most

  • Requires smoothing constant (W)
  • Ranges from 0 to 1
  • Subjectively chosen

  • Involves little record keeping of past data

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58

You’re organizing a Kwanza meeting. You want to forecast attendance for 1998 using exponential smoothing
( = .20). Past attendance (00) is:
1995 4
1996 6
1997 5
1998 3
1999 7

Exponential Smoothing
[An Example]

© 1995 Corel Corp.

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63

Exponential Smoothing

Ei = W·Yi + (1 - W)·Ei-1

^

Time

Yi

Smoothed Value, Ei (W = .2)

Forecast Yi + 1

1995

4

4.0

NA

1996

6

(.2)(6) + (1-.2)(4.0) = 4.4

4.0

1997

5

(.2)(5) + (1-.2)(4.4) = 4.5

4.4

1998

3

(.2)(3) + (1-.2)(4.5) = 4.2

4.5

1999

7

(.2)(7) + (1-.2)(4.2) = 4.8

4.2

2000

NA

NA

4.8

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81

Exponential Smoothing [Graph]

Year

Attendance

Actual

*

82

Forecast Effect of Smoothing Coefficient (W)

Yi+1 = W·Yi + W·(1-W)·Yi-1 + W·(1-W)2·Yi-2 +...

^

Weight�

W is...�

Prior Period�

2 Periods Ago�

3 Periods Ago�

W�

W(1-W)�

W(1-W)2�

0.10�

10%�

9%�

8.1%�

0.90�

90%�

9%�

0.9%�

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86

Time Series Forecasting

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88

Linear Time-Series Forecasting Model

Used for forecasting trend

Relationship between response variable Y & time X is a linear function

Coded X values used often

  • Year X: 1995 1996 1997 1998 1999
  • Coded year: 0 1 2 3 4
  • Sales Y: 78.7 63.5 89.7 93.2 92.1

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89

Linear Time-Series Model

b1 > 0

b1 < 0

*

90

Linear Time-Series Model [An Example]

You’re a marketing analyst for Hasbro Toys. Using coded years, you find Yi = .6 + .7Xi.

1995 1
1996 1
1997 2
1998 2
1999 4

Forecast 2000 sales.

^

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91

Linear Time-Series [Example]

Year Coded Year Sales (Units)
1995 0 1
1996 1 1
1997 2 2
1998 3 2
1999 4 4
2000 5 ?

2000 forecast sales: Yi = .6 + .7·(5) = 4.1

The equation would be different if ‘Year’ used.

^

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92

The Linear Trend Model

Year Coded Sales

94 0 2

95 1 5

96 2 2

97 3 2

98 4 7

99 5 6

Projected to year 2000

Excel Output

Chart6

2
5
2
2
7
6
Sales

Sheet1

Year Sales
1994 2
1995 5
1996 2
1997 2
1998 7
1999 6
0

Sheet1

0
0
0
0
0
0
Sales

Sheet2

Sheet3

Sheet6

SUMMARY OUTPUT
Regression Statistics
Multiple R 0.667413042
R Square 0.4454401687
Adjusted R Square 0.0757336145
Standard Error 1.7985917684
Observations 6
ANOVA
df SS MS F Significance F
Regression 2 7.795202952 3.897601476 1.2048479087 0.4129739079
Residual 3 9.704797048 3.2349323493
Total 5 17.5
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 3.4169741697 4.8766307606 0.7006833893 0.5339558989 -12.1026559357 18.9366042752 -12.1026559357 18.9366042752
X Variable 1 -1.3265682657 2.9081572319 -0.4561542447 0.6792756863 -10.5816311856 7.9284946543 -10.5816311856 7.9284946543
X Variable 2 0.2158671587 0.3411538399 0.6327560574 0.5718314052 -0.8698376371 1.3015719544 -0.8698376371 1.3015719544
RESIDUAL OUTPUT
Observation Predicted Y Residuals Standard Residuals
1 1.6273062731 -1.6273062731 -1.1680490269
2 2.1808118081 -1.1808118081 -0.8475639198
3 1.6273062731 0.3726937269 0.2675123622
4 1.6273062731 1.3726937269 0.9852930567
5 4.7084870849 -0.7084870849 -0.5085383519
6 3.2287822878 1.7712177122 1.2713458797

Sheet6

-1.6273062731
-1.1808118081
0.3726937269
1.3726937269
-0.7084870849
1.7712177122
X Variable 1
Residuals
X Variable 1 Residual Plot

Sheet7

-1.6273062731
-1.1808118081
0.3726937269
1.3726937269
-0.7084870849
1.7712177122
X Variable 2
Residuals
X Variable 2 Residual Plot

Sheet8

0 1.6273062731
1 2.1808118081
2 1.6273062731
3 1.6273062731
4 4.7084870849
5 3.2287822878
Y
Predicted Y
X Variable 1
Y
X Variable 1 Line Fit Plot

Sheet10

0 1.6273062731
1 2.1808118081
2 1.6273062731
3 1.6273062731
4 4.7084870849
5 3.2287822878
Y
Predicted Y
X Variable 2
Y
X Variable 2 Line Fit Plot

Sheet4

SUMMARY OUTPUT
Regression Statistics
Multiple R 0.5735253764
R Square 0.3289313574
Adjusted R Square 0.1611641968
Standard Error 0.2410543825
Observations 6
ANOVA
df SS MS F Significance F
Regression 1 0.1139274524 0.1139274524 1.9606420955 0.2340371757
Residual 4 0.2324288613 0.0581072153
Total 5 0.3463563137
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 0.3358379483 0.1744623535 1.9249880653 0.1265429144 -0.1485482024 0.8202240991 -0.1485482024 0.8202240991
X Variable 1 0.0806854394 0.0576230189 1.4002293011 0.2340371757 -0.0793020406 0.2406729195 -0.0793020406 0.2406729195
RESIDUAL OUTPUT antilog(.33583795) = 2.1668954035
antilog(.08068544) = 1.2041634459
Observation Predicted Y Residuals Standard Residuals
1 0.3358379483 -0.0348079527 -0.1614427158
2 0.4165233878 0.2824466166 1.3100152506
3 0.4972088272 -0.1961788316 -0.9098967597
4 0.5778942667 -0.276864271 -1.2841237817
5 0.6585797061 0.1865183339 0.8650904192
6 0.7392651456 0.0388861048 0.1803575874

Sheet4

-0.0348079527
0.2824466166
-0.1961788316
-0.276864271
0.1865183339
0.0388861048
X Variable 1
Residuals
X Variable 1 Residual Plot

Sheet5

0.3010299957 0.3358379483
0.6989700043 0.4165233878
0.3010299957 0.4972088272
0.3010299957 0.5778942667
0.84509804 0.6585797061
0.7781512504 0.7392651456
Y
Predicted Y
X Variable 1
Y
X Variable 1 Line Fit Plot

Sheet1

SUMMARY OUTPUT
Regression Statistics
Multiple R 0.0377358491
R Square 0.0014239943
Adjusted R Square -0.3314346743
Standard Error 2.6564268809
Observations 5
ANOVA
df SS MS F Significance F
Regression 1 0.0301886792 0.0301886792 0.0042780749 0.9519646302
Residual 3 21.1698113208 7.0566037736
Total 4 21.2
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 4.2641509434 2.3927325591 1.7821260162 0.172748462 -3.3505990923 11.8789009791 -3.3505990923 11.8789009791
X Variable 1 0.0377358491 0.576939051 0.065406994 0.9519646302 -1.7983434246 1.8738151227 -1.7983434246 1.8738151227
RESIDUAL OUTPUT
Observation Predicted Y Residuals Standard Residuals
1 4.3396226415 0.6603773585 0.2870540488
2 4.4528301887 -2.4528301887 -1.0662007525
3 4.3396226415 -2.3396226415 -1.016991487
4 4.3396226415 2.6603773585 1.1564177393
5 4.5283018868 1.4716981132 0.6397204515

Sheet1

0.6603773585
-2.4528301887
-2.3396226415
2.6603773585
1.4716981132
X Variable 1
Residuals
X Variable 1 Residual Plot

Sheet9

5 4.3396226415
2 4.4528301887
2 4.3396226415
7 4.3396226415
6 4.5283018868
Y
Predicted Y
X Variable 1
Y
X Variable 1 Line Fit Plot

Sheet11

Sheet12

SUMMARY OUTPUT
Regression Statistics
Multiple R 0.6094494002
R Square 0.3714285714
Adjusted R Square 0.2142857143
Standard Error 2.0213149892
Observations 6
ANOVA
df SS MS F Significance F
Regression 1 9.6571428571 9.6571428571 2.3636363636 0.1990093594
Residual 4 16.3428571429 4.0857142857
Total 5 26
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept -1479.1142860169 964.6826010322 -1.5332652257 0.1999860798 -4157.5081193151 1199.2795472814 -4157.5081193151 1199.2795472814
X Variable 1 0.742857143 0.4831867008 1.5374122297 0.1990093593 -0.5986869859 2.0844012719 -0.5986869859 2.0844012719
RESIDUAL OUTPUT
Observation Predicted Y Residuals
1 2.1428571425 -0.1428571425
2 2.8857142855 2.1142857145
3 3.6285714285 -1.6285714285
4 4.3714285715 -2.3714285715
5 5.1142857145 1.8857142855
6 5.8571428575 0.1428571425

Sheet2

SUMMARY OUTPUT
Coded Linear Trend
Regression Statistics
Multiple R 0.6094494002
R Square 0.3714285714
Adjusted R Square 0.2142857143
Standard Error 2.0213149892
Observations 6
ANOVA
df SS MS F Significance F
Regression 1 9.6571428571 9.6571428571 2.3636363636 0.1990093594
Residual 4 16.3428571429 4.0857142857
Total 5 26
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 2.1428571429 1.4629203855 1.4647804242 0.21684065 -1.9188694137 6.2045836994 -1.9188694137 6.2045836994
X Variable 1 0.7428571429 0.4831867007 1.5374122296 0.1990093594 -0.5986869859 2.0844012716 -0.5986869859 2.0844012716
RESIDUAL OUTPUT
Observation Predicted Y Residuals
1 2.1428571429 -0.1428571429
2 2.8857142857 2.1142857143
3 3.6285714286 -1.6285714286
4 4.3714285714 -2.3714285714
5 5.1142857143 1.8857142857
6 5.8571428571 0.1428571429

Sheet3

Year Sales 1st Coded Sales Sales^2 log(sales)
1994 0 2 0 2 4 0.3010299957
1995 1 5 2 1 5 25 0.6989700043
1996 2 2 5 2 2 4 0.3010299957
1997 3 2 2 3 2 4 0.3010299957
1998 4 7 2 4 7 49 0.84509804
1999 5 6 7 5 6 36 0.7781512504
2000

Sheet3

0
0
0
0
0
0
Sales
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.3977058393
R Square 0.1581699346
Adjusted R Square -0.0101960784
Standard Error 1.4712939483
Observations 7
ANOVA
df SS MS F Significance F
Regression 1 2.0336134454 2.0336134454 0.9394409938 0.3769372438
Residual 5 10.8235294118 2.1647058824
Total 6 12.8571428571
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 1.2941176471 1.9868077019 0.6513552599 0.5435635839 -3.8131257969 6.401361091 -3.8131257969 6.401361091
X Variable 1 0.6470588235 0.667588751 0.9692476432 0.3769372438 -1.0690298893 2.3631475364 -1.0690298893 2.3631475364
RESIDUAL OUTPUT
Observation Predicted Y Residuals Standard Residuals
1 3.8823529412 -0.8823529412 -0.6569518078
2 3.2352941176 -1.2352941176 -0.9197325309
3 2.5882352941 0.4117647059 0.3065775103
4 3.2352941176 -1.2352941176 -0.9197325309
5 2.5882352941 -0.5882352941 -0.4379678719
6 2.5882352941 1.4117647059 1.0511228925
7 3.8823529412 2.1176470588 1.5766843387
0
0
0
0
0
0
0
X Variable 1
Residuals
X Variable 1 Residual Plot
3 0
2 0
3 0
2 0
2 0
4 0
6 0
Y
Predicted Y
X Variable 1
Y
X Variable 1 Line Fit Plot
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.6920231961
R Square 0.4788961039
Adjusted R Square 0.1314935065
Standard Error 1.4930394056
Observations 6
ANOVA
df SS MS F Significance F
Regression 2 6.1458333333 3.0729166667 1.3785046729 0.3761720123
Residual 3 6.6875 2.2291666667
Total 5 12.8333333333
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 3.5 3.501487779 0.9995751009 0.3911779502 -7.643307299 14.643307299 -7.643307299 14.643307299
X Variable 1 0.8125 0.834634401 0.9734801238 0.4021141357 -1.8436816575 3.4686816575 -1.8436816575 3.4686816575
X Variable 2 -0.9375 0.834634401 -1.1232462967 0.3431108215 -3.5936816575 1.7186816575 -3.5936816575 1.7186816575
4.625
RESIDUAL OUTPUT
Observation Predicted Y Residuals Standard Residuals
1 2.1875 -0.1875 -0.1621266379
2 2.3125 0.6875 0.5944643391
3 4.0625 -2.0625 -1.7833930173
4 2.3125 -0.3125 -0.2702110632
5 3.25 0.75 0.6485065518
6 4.875 1.125 0.9727598276
0
0
0
0
0
0
X Variable 1
Residuals
X Variable 1 Residual Plot
-0.1875
0.6875
-2.0625
-0.3125
0.75
1.125
X Variable 2
Residuals
X Variable 2 Residual Plot
2 2.1875
3 2.3125
2 4.0625
2 2.3125
4 3.25
6 4.875
Y
Predicted Y
X Variable 1
Y
X Variable 1 Line Fit Plot
2 2.1875
3 2.3125
2 4.0625
2 2.3125
4 3.25
6 4.875
Y
Predicted Y
X Variable 2
Y
X Variable 2 Line Fit Plot
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.6477984695
R Square 0.4196428571
Adjusted R Square -1.3214285714
Standard Error 2.5495097568
Observations 5
ANOVA
df SS MS F Significance F
Regression 3 4.7 1.5666666667 0.241025641 0.8655519358
Residual 1 6.5 6.5
Total 4 11.2
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 2 13.2098448136 0.1514022328 0.9043408499 -165.8462736521 169.8462736521 -165.8462736521 169.8462736521
X Variable 1 1 2 0.5 0.7048327648 -24.4123006016 26.4123006016 -24.4123006016 26.4123006016
X Variable 2 -0.5 2.9580398915 -0.1690308509 0.8933992419 -38.0852994578 37.0852994578 -38.0852994578 37.0852994578
X Variable 3 0 1.7320508076 0 1 -22.0076978896 22.0076978896 -22.0076978896 22.0076978896
RESIDUAL OUTPUT
Observation Predicted Y Residuals Standard Residuals
1 2.5 0.5 0.3922322703
2 4 -2 -1.5689290811
3 2.5 -0.5 -0.3922322703
4 3 1 0.7844645406
5 5 1 0.7844645406
0
0
0
0
0
X Variable 1
Residuals
X Variable 1 Residual Plot
0
0
0
0
0
X Variable 2
Residuals
X Variable 2 Residual Plot
0.5
-2
-0.5
1
1
X Variable 3
Residuals
X Variable 3 Residual Plot
3 2.5
2 4
2 2.5
4 3
6 5
Y
Predicted Y
X Variable 1
Y
X Variable 1 Line Fit Plot
3 2.5
2 4
2 2.5
4 3
6 5
Y
Predicted Y
X Variable 2
Y
X Variable 2 Line Fit Plot
3 2.5
2 4
2 2.5
4 3
6 5
Y
Predicted Y
X Variable 3
Y
X Variable 3 Line Fit Plot
1 4
2 3 4
3 2 3 4
3 3 2 3 4
4 2 3 2 3
5 2 2 3 2
6 4 2 2 3
7 6 4 2 2

*

Quadratic Time-Series Forecasting Model

*

93

Time Series Forecasting

*

94

Quadratic Time-Series Forecasting Model

  • Used for forecasting trend
  • Relationship between response variable Y & time X is a quadratic function
  • Coded years used

*

95

Quadratic Time-Series Forecasting Model

  • Used for forecasting trend
  • Relationship between response variable Y & time X is a quadratic function
  • Coded years used
  • Quadratic model

*

96

Quadratic Trend Model

Excel Output

Year Coded Sales

94 0 2

95 1 5

96 2 2

97 3 2

98 4 7

99 5 6

Sheet6

SUMMARY OUTPUT
Regression Statistics
Multiple R 0.667413042
R Square 0.4454401687
Adjusted R Square 0.0757336145
Standard Error 1.7985917684
Observations 6
ANOVA
df SS MS F Significance F
Regression 2 7.795202952 3.897601476 1.2048479087 0.4129739079
Residual 3 9.704797048 3.2349323493
Total 5 17.5
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 3.4169741697 4.8766307606 0.7006833893 0.5339558989 -12.1026559357 18.9366042752 -12.1026559357 18.9366042752
X Variable 1 -1.3265682657 2.9081572319 -0.4561542447 0.6792756863 -10.5816311856 7.9284946543 -10.5816311856 7.9284946543
X Variable 2 0.2158671587 0.3411538399 0.6327560574 0.5718314052 -0.8698376371 1.3015719544 -0.8698376371 1.3015719544
RESIDUAL OUTPUT
Observation Predicted Y Residuals Standard Residuals
1 1.6273062731 -1.6273062731 -1.1680490269
2 2.1808118081 -1.1808118081 -0.8475639198
3 1.6273062731 0.3726937269 0.2675123622
4 1.6273062731 1.3726937269 0.9852930567
5 4.7084870849 -0.7084870849 -0.5085383519
6 3.2287822878 1.7712177122 1.2713458797

Sheet6

-1.6273062731
-1.1808118081
0.3726937269
1.3726937269
-0.7084870849
1.7712177122
X Variable 1
Residuals
X Variable 1 Residual Plot

Sheet7

-1.6273062731
-1.1808118081
0.3726937269
1.3726937269
-0.7084870849
1.7712177122
X Variable 2
Residuals
X Variable 2 Residual Plot

Sheet8

0 1.6273062731
1 2.1808118081
2 1.6273062731
3 1.6273062731
4 4.7084870849
5 3.2287822878
Y
Predicted Y
X Variable 1
Y
X Variable 1 Line Fit Plot

Sheet10

0 1.6273062731
1 2.1808118081
2 1.6273062731
3 1.6273062731
4 4.7084870849
5 3.2287822878
Y
Predicted Y
X Variable 2
Y
X Variable 2 Line Fit Plot

Sheet4

SUMMARY OUTPUT
Regression Statistics
Multiple R 0.5735253764
R Square 0.3289313574
Adjusted R Square 0.1611641968
Standard Error 0.2410543825
Observations 6
ANOVA
df SS MS F Significance F
Regression 1 0.1139274524 0.1139274524 1.9606420955 0.2340371757
Residual 4 0.2324288613 0.0581072153
Total 5 0.3463563137
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 0.3358379483 0.1744623535 1.9249880653 0.1265429144 -0.1485482024 0.8202240991 -0.1485482024 0.8202240991
X Variable 1 0.0806854394 0.0576230189 1.4002293011 0.2340371757 -0.0793020406 0.2406729195 -0.0793020406 0.2406729195
RESIDUAL OUTPUT antilog(.33583795) = 2.1668954035
antilog(.08068544) = 1.2041634459
Observation Predicted Y Residuals Standard Residuals
1 0.3358379483 -0.0348079527 -0.1614427158
2 0.4165233878 0.2824466166 1.3100152506
3 0.4972088272 -0.1961788316 -0.9098967597
4 0.5778942667 -0.276864271 -1.2841237817
5 0.6585797061 0.1865183339 0.8650904192
6 0.7392651456 0.0388861048 0.1803575874

Sheet4

-0.0348079527
0.2824466166
-0.1961788316
-0.276864271
0.1865183339
0.0388861048
X Variable 1
Residuals
X Variable 1 Residual Plot

Sheet5

0.3010299957 0.3358379483
0.6989700043 0.4165233878
0.3010299957 0.4972088272
0.3010299957 0.5778942667
0.84509804 0.6585797061
0.7781512504 0.7392651456
Y
Predicted Y
X Variable 1
Y
X Variable 1 Line Fit Plot

Sheet13

SUMMARY OUTPUT
Regression Statistics
Multiple R 0.0377358491
R Square 0.0014239943
Adjusted R Square -0.3314346743
Standard Error 2.6564268809
Observations 5
ANOVA
df SS MS F Significance F
Regression 1 0.0301886792 0.0301886792 0.0042780749 0.9519646302
Residual 3 21.1698113208 7.0566037736
Total 4 21.2
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 4.2641509434 2.3927325591 1.7821260162 0.172748462 -3.3505990923 11.8789009791 -3.3505990923 11.8789009791
X Variable 1 0.0377358491 0.576939051 0.065406994 0.9519646302 -1.7983434246 1.8738151227 -1.7983434246 1.8738151227
RESIDUAL OUTPUT
Observation Predicted Y Residuals Standard Residuals
1 4.3396226415 0.6603773585 0.2870540488
2 4.4528301887 -2.4528301887 -1.0662007525
3 4.3396226415 -2.3396226415 -1.016991487
4 4.3396226415 2.6603773585 1.1564177393
5 4.5283018868 1.4716981132 0.6397204515

Sheet13

0.6603773585
-2.4528301887
-2.3396226415
2.6603773585
1.4716981132
X Variable 1
Residuals
X Variable 1 Residual Plot

Sheet1

5 4.3396226415
2 4.4528301887
2 4.3396226415
7 4.3396226415
6 4.5283018868
Y
Predicted Y
X Variable 1
Y
X Variable 1 Line Fit Plot

Sheet9

Sheet11

SUMMARY OUTPUT
Regression Statistics
Multiple R 0.6094494002
R Square 0.3714285714
Adjusted R Square 0.2142857143
Standard Error 2.0213149892
Observations 6
ANOVA
df SS MS F Significance F
Regression 1 9.6571428571 9.6571428571 2.3636363636 0.1990093594
Residual 4 16.3428571429 4.0857142857
Total 5 26
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept -1479.1142860169 964.6826010322 -1.5332652257 0.1999860798 -4157.5081193151 1199.2795472814 -4157.5081193151 1199.2795472814
X Variable 1 0.742857143 0.4831867008 1.5374122297 0.1990093593 -0.5986869859 2.0844012719 -0.5986869859 2.0844012719
RESIDUAL OUTPUT
Observation Predicted Y Residuals
1 2.1428571425 -0.1428571425
2 2.8857142855 2.1142857145
3 3.6285714285 -1.6285714285
4 4.3714285715 -2.3714285715
5 5.1142857145 1.8857142855
6 5.8571428575 0.1428571425

Sheet12

SUMMARY OUTPUT
Coded Linear Trend
Regression Statistics
Multiple R 0.6094494002
R Square 0.3714285714
Adjusted R Square 0.2142857143
Standard Error 2.0213149892
Observations 6
ANOVA
df SS MS F Significance F
Regression 1 9.6571428571 9.6571428571 2.3636363636 0.1990093594
Residual 4 16.3428571429 4.0857142857
Total 5 26
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 2.1428571429 1.4629203855 1.4647804242 0.21684065 -1.9188694137 6.2045836994 -1.9188694137 6.2045836994
X Variable 1 0.7428571429 0.4831867007 1.5374122296 0.1990093594 -0.5986869859 2.0844012716 -0.5986869859 2.0844012716
RESIDUAL OUTPUT
Observation Predicted Y Residuals
1 2.1428571429 -0.1428571429
2 2.8857142857 2.1142857143
3 3.6285714286 -1.6285714286
4 4.3714285714 -2.3714285714
5 5.1142857143 1.8857142857
6 5.8571428571 0.1428571429

Sheet2

SUMMARY OUTPUT
Regression Statistics
Multiple R 0.6613339832
R Square 0.4373626374
Adjusted R Square 0.0622710623
Standard Error 2.2082097899
Observations 6
ANOVA
df SS MS F Significance F
Regression 2 11.3714285714 5.6857142857 1.166015625 0.4220295409
Residual 3 14.6285714286 4.8761904762
Total 5 26
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 2.8571428571 2.0013600818 1.4276006018 0.2486994856 -3.5120841156 9.2263698299 -3.5120841156 9.2263698299
X Variable 1 -0.3285714286 1.8825370058 -0.1745365045 0.8725579994 -6.3196499875 5.6625071304 -6.3196499875 5.6625071304
X Variable 2 0.2142857143 0.3614031612 0.5929270613 0.5949565566 -0.9358615196 1.3644329482 -0.9358615196 1.3644329482
RESIDUAL OUTPUT
Observation Predicted Y Residuals
1 2.8571428571 -0.8571428571
2 2.7428571429 2.2571428571
3 3.0571428571 -1.0571428571
4 3.8 -1.8
5 4.9714285714 2.0285714286
6 6.5714285714 -0.5714285714

Sheet3

Year Sales 1st Coded coded^2 log(sales)
1994 0 2 0 0 0
1995 1 5 2 1 1 0
1996 2 2 5 2 4 0.6020599913
1997 3 2 2 3 9 0.9542425094
1998 4 7 2 4 16 1.2041199827
1999 5 6 7 5 25 1.3979400087
2000

Sheet3

0
0
0
0
0
0
Sales
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.3977058393
R Square 0.1581699346
Adjusted R Square -0.0101960784
Standard Error 1.4712939483
Observations 7
ANOVA
df SS MS F Significance F
Regression 1 2.0336134454 2.0336134454 0.9394409938 0.3769372438
Residual 5 10.8235294118 2.1647058824
Total 6 12.8571428571
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 1.2941176471 1.9868077019 0.6513552599 0.5435635839 -3.8131257969 6.401361091 -3.8131257969 6.401361091
X Variable 1 0.6470588235 0.667588751 0.9692476432 0.3769372438 -1.0690298893 2.3631475364 -1.0690298893 2.3631475364
RESIDUAL OUTPUT
Observation Predicted Y Residuals Standard Residuals
1 3.8823529412 -0.8823529412 -0.6569518078
2 3.2352941176 -1.2352941176 -0.9197325309
3 2.5882352941 0.4117647059 0.3065775103
4 3.2352941176 -1.2352941176 -0.9197325309
5 2.5882352941 -0.5882352941 -0.4379678719
6 2.5882352941 1.4117647059 1.0511228925
7 3.8823529412 2.1176470588 1.5766843387
0
0
0
0
0
0
0
X Variable 1
Residuals
X Variable 1 Residual Plot
3 0
2 0
3 0
2 0
2 0
4 0
6 0
Y
Predicted Y
X Variable 1
Y
X Variable 1 Line Fit Plot
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.6920231961
R Square 0.4788961039
Adjusted R Square 0.1314935065
Standard Error 1.4930394056
Observations 6
ANOVA
df SS MS F Significance F
Regression 2 6.1458333333 3.0729166667 1.3785046729 0.3761720123
Residual 3 6.6875 2.2291666667
Total 5 12.8333333333
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 3.5 3.501487779 0.9995751009 0.3911779502 -7.643307299 14.643307299 -7.643307299 14.643307299
X Variable 1 0.8125 0.834634401 0.9734801238 0.4021141357 -1.8436816575 3.4686816575 -1.8436816575 3.4686816575
X Variable 2 -0.9375 0.834634401 -1.1232462967 0.3431108215 -3.5936816575 1.7186816575 -3.5936816575 1.7186816575
4.625
RESIDUAL OUTPUT
Observation Predicted Y Residuals Standard Residuals
1 2.1875 -0.1875 -0.1621266379
2 2.3125 0.6875 0.5944643391
3 4.0625 -2.0625 -1.7833930173
4 2.3125 -0.3125 -0.2702110632
5 3.25 0.75 0.6485065518
6 4.875 1.125 0.9727598276
0
0
0
0
0
0
X Variable 1
Residuals
X Variable 1 Residual Plot
-0.1875
0.6875
-2.0625
-0.3125
0.75
1.125
X Variable 2
Residuals
X Variable 2 Residual Plot
2 2.1875
3 2.3125
2 4.0625
2 2.3125
4 3.25
6 4.875
Y
Predicted Y
X Variable 1
Y
X Variable 1 Line Fit Plot
2 2.1875
3 2.3125
2 4.0625
2 2.3125
4 3.25
6 4.875
Y
Predicted Y
X Variable 2
Y
X Variable 2 Line Fit Plot
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.6477984695
R Square 0.4196428571
Adjusted R Square -1.3214285714
Standard Error 2.5495097568
Observations 5
ANOVA
df SS MS F Significance F
Regression 3 4.7 1.5666666667 0.241025641 0.8655519358
Residual 1 6.5 6.5
Total 4 11.2
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 2 13.2098448136 0.1514022328 0.9043408499 -165.8462736521 169.8462736521 -165.8462736521 169.8462736521
X Variable 1 1 2 0.5 0.7048327648 -24.4123006016 26.4123006016 -24.4123006016 26.4123006016
X Variable 2 -0.5 2.9580398915 -0.1690308509 0.8933992419 -38.0852994578 37.0852994578 -38.0852994578 37.0852994578
X Variable 3 0 1.7320508076 0 1 -22.0076978896 22.0076978896 -22.0076978896 22.0076978896
RESIDUAL OUTPUT
Observation Predicted Y Residuals Standard Residuals
1 2.5 0.5 0.3922322703
2 4 -2 -1.5689290811
3 2.5 -0.5 -0.3922322703
4 3 1 0.7844645406
5 5 1 0.7844645406
0
0
0
0
0
X Variable 1
Residuals
X Variable 1 Residual Plot
0
0
0
0
0
X Variable 2
Residuals
X Variable 2 Residual Plot
0.5
-2
-0.5
1
1
X Variable 3
Residuals
X Variable 3 Residual Plot
3 2.5
2 4
2 2.5
4 3
6 5
Y
Predicted Y
X Variable 1
Y
X Variable 1 Line Fit Plot
3 2.5
2 4
2 2.5
4 3
6 5
Y
Predicted Y
X Variable 2
Y
X Variable 2 Line Fit Plot
3 2.5
2 4
2 2.5
4 3
6 5
Y
Predicted Y
X Variable 3
Y
X Variable 3 Line Fit Plot
1 4
2 3 4
3 2 3 4
3 3 2 3 4
4 2 3 2 3
5 2 2 3 2
6 4 2 2 3
7 6 4 2 2

*

Time Series Forecasting

*

99

Exponential Time-Series Forecasting Model

  • Used for forecasting trend
  • Relationship is an exponential function
  • Series increases (decreases) at increasing (decreasing) rate

*

101

Exponential Trend Model

or

Excel Output of Values in logs

Year Coded Sales

94 0 2

95 1 5

96 2 2

97 3 2

98 4 7

99 5 6

Sheet6

SUMMARY OUTPUT
Regression Statistics
Multiple R 0.667413042
R Square 0.4454401687
Adjusted R Square 0.0757336145
Standard Error 1.7985917684
Observations 6
ANOVA
df SS MS F Significance F
Regression 2 7.795202952 3.897601476 1.2048479087 0.4129739079
Residual 3 9.704797048 3.2349323493
Total 5 17.5
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 3.4169741697 4.8766307606 0.7006833893 0.5339558989 -12.1026559357 18.9366042752 -12.1026559357 18.9366042752
X Variable 1 -1.3265682657 2.9081572319 -0.4561542447 0.6792756863 -10.5816311856 7.9284946543 -10.5816311856 7.9284946543
X Variable 2 0.2158671587 0.3411538399 0.6327560574 0.5718314052 -0.8698376371 1.3015719544 -0.8698376371 1.3015719544
RESIDUAL OUTPUT
Observation Predicted Y Residuals Standard Residuals
1 1.6273062731 -1.6273062731 -1.1680490269
2 2.1808118081 -1.1808118081 -0.8475639198
3 1.6273062731 0.3726937269 0.2675123622
4 1.6273062731 1.3726937269 0.9852930567
5 4.7084870849 -0.7084870849 -0.5085383519
6 3.2287822878 1.7712177122 1.2713458797

Sheet6

2
5
2
2
7
6
X Variable 1
Residuals
X Variable 1 Residual Plot

Sheet7

4
25
4
4
49
36
X Variable 2
Residuals
X Variable 2 Residual Plot

Sheet1

0 2
1 5
2 2
3 2
4 7
5 6
Y
Predicted Y
X Variable 1
Y
X Variable 1 Line Fit Plot

Sheet2

0 4
1 25
2 4
3 4
4 49
5 36
Y
Predicted Y
X Variable 2
Y
X Variable 2 Line Fit Plot

Sheet3

SUMMARY OUTPUT
Regression Statistics
Multiple R 0.5735253764
R Square 0.3289313574
Adjusted R Square 0.1611641968
Standard Error 0.2410543825
Observations 6
ANOVA
df SS MS F Significance F
Regression 1 0.1139274524 0.1139274524 1.9606420955 0.2340371757
Residual 4 0.2324288613 0.0581072153
Total 5 0.3463563137
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 0.3358379483 0.1744623535 1.9249880653 0.1265429144 -0.1485482024 0.8202240991 -0.1485482024 0.8202240991
X Variable 1 0.0806854394 0.0576230189 1.4002293011 0.2340371757 -0.0793020406 0.2406729195 -0.0793020406 0.2406729195
RESIDUAL OUTPUT
Observation Predicted Y Residuals Standard Residuals
1 0.3358379483 -0.0348079527 -0.1614427158
2 0.4165233878 0.2824466166 1.3100152506
3 0.4972088272 -0.1961788316 -0.9098967597
4 0.5778942667 -0.276864271 -1.2841237817
5 0.6585797061 0.1865183339 0.8650904192
6 0.7392651456 0.0388861048 0.1803575874

Sheet3

0
1
2
3
4
5
X Variable 1
Residuals
X Variable 1 Residual Plot
0.3010299957 0
0.6989700043 1
0.3010299957 2
0.3010299957 3
0.84509804 4
0.7781512504 5
Y
Predicted Y
X Variable 1
Y
X Variable 1 Line Fit Plot
Year Sales Coded Sales Sales^2 log(sales)
1994 2 0 2 4 0.3010299957
1995 5 1 5 25 0.6989700043
1996 2 2 2 4 0.3010299957
1997 2 3 2 4 0.3010299957
1998 7 4 7 49 0.84509804
1999 6 5 6 36 0.7781512504
0
Sales

Sheet6

SUMMARY OUTPUT
Regression Statistics
Multiple R 0.667413042
R Square 0.4454401687
Adjusted R Square 0.0757336145
Standard Error 1.7985917684
Observations 6
ANOVA
df SS MS F Significance F
Regression 2 7.795202952 3.897601476 1.2048479087 0.4129739079
Residual 3 9.704797048 3.2349323493
Total 5 17.5
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 3.4169741697 4.8766307606 0.7006833893 0.5339558989 -12.1026559357 18.9366042752 -12.1026559357 18.9366042752
X Variable 1 -1.3265682657 2.9081572319 -0.4561542447 0.6792756863 -10.5816311856 7.9284946543 -10.5816311856 7.9284946543
X Variable 2 0.2158671587 0.3411538399 0.6327560574 0.5718314052 -0.8698376371 1.3015719544 -0.8698376371 1.3015719544
RESIDUAL OUTPUT
Observation Predicted Y Residuals Standard Residuals
1 1.6273062731 -1.6273062731 -1.1680490269
2 2.1808118081 -1.1808118081 -0.8475639198
3 1.6273062731 0.3726937269 0.2675123622
4 1.6273062731 1.3726937269 0.9852930567
5 4.7084870849 -0.7084870849 -0.5085383519
6 3.2287822878 1.7712177122 1.2713458797

Sheet6

2
5
2
2
7
6
X Variable 1
Residuals
X Variable 1 Residual Plot

Sheet7

4
25
4
4
49
36
X Variable 2
Residuals
X Variable 2 Residual Plot

Sheet1

0 2
1 5
2 2
3 2
4 7
5 6
Y
Predicted Y
X Variable 1
Y
X Variable 1 Line Fit Plot

Sheet2

0 4
1 25
2 4
3 4
4 49
5 36
Y
Predicted Y
X Variable 2
Y
X Variable 2 Line Fit Plot

Sheet3

SUMMARY OUTPUT
Regression Statistics
Multiple R 0.5735253764
R Square 0.3289313574
Adjusted R Square 0.1611641968
Standard Error 0.2410543825
Observations 6
ANOVA
df SS MS F Significance F
Regression 1 0.1139274524 0.1139274524 1.9606420955 0.2340371757
Residual 4 0.2324288613 0.0581072153
Total 5 0.3463563137
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 0.3358379483 0.1744623535 1.9249880653 0.1265429144 -0.1485482024 0.8202240991 -0.1485482024 0.8202240991
X Variable 1 0.0806854394 0.0576230189 1.4002293011 0.2340371757 -0.0793020406 0.2406729195 -0.0793020406 0.2406729195
RESIDUAL OUTPUT antilog(.33583795) = 2.1668954035
antilog(.08068544) = 1.2041634459
Observation Predicted Y Residuals Standard Residuals
1 0.3358379483 -0.0348079527 -0.1614427158
2 0.4165233878 0.2824466166 1.3100152506
3 0.4972088272 -0.1961788316 -0.9098967597
4 0.5778942667 -0.276864271 -1.2841237817
5 0.6585797061 0.1865183339 0.8650904192
6 0.7392651456 0.0388861048 0.1803575874

Sheet3

0
1
2
3
4
5
X Variable 1
Residuals
X Variable 1 Residual Plot
0.3010299957 0
0.6989700043 1
0.3010299957 2
0.3010299957 3
0.84509804 4
0.7781512504 5
Y
Predicted Y
X Variable 1
Y
X Variable 1 Line Fit Plot
Year Sales Coded Sales Sales^2 log(sales)
1994 2 0 2 4 0.3010299957
1995 5 1 5 25 0.6989700043
1996 2 2 2 4 0.3010299957
1997 2 3 2 4 0.3010299957
1998 7 4 7 49 0.84509804
1999 6 5 6 36 0.7781512504
0
Sales

*

Time Series Forecasting

*

104

Autoregressive Modeling

  • Used for forecasting trend
  • Like regression model
  • Independent variables are lagged response variables Yi-1, Yi-2, Yi-3 etc.
  • Assumes data are correlated with past data values
  • 1st Order: Correlated with prior period
  • Estimate with ordinary least squares

*

105

Time Series Data Plot

���������

Data collected by Coop Student (10/6/95)

Intra-Campus Bus Passengers

(X 1000)

Number of Passengers

0

2

4

6

8

10

12

Month/Year

09/83

07/86

05/89

03/92

01/95

Auto-correlation Plot

( 2 (

Intra-Campus Bus Passengers

(Auto Correlation Function Plot

-1

-0.5

0

0.5

1

Lag

0

5

10

15

20

25

Autoregressive Model [An Example]

The Office Concept Corp. has acquired a number of office units (in thousands of square feet) over the last 8 years. Develop the 2nd order Autoregressive models.

Year Units

92 4

93 3

94 2

95 3

96 2

97 2

98 4

99 6

*

Autoregressive Model [Example Solution]

Year Yi Yi-1 Yi-2

92 4 --- ---

93 3 4 ---

94 2 3 4

95 3 2 3

96 2 3 2

97 2 2 3

98 4 2 2

99 6 4 2

Excel Output

Develop the 2nd order table

Use Excel to run a regression model

Sheet6

SUMMARY OUTPUT
Regression Statistics
Multiple R 0.667413042
R Square 0.4454401687
Adjusted R Square 0.0757336145
Standard Error 1.7985917684
Observations 6
ANOVA
df SS MS F Significance F
Regression 2 7.795202952 3.897601476 1.2048479087 0.4129739079
Residual 3 9.704797048 3.2349323493
Total 5 17.5
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 3.4169741697 4.8766307606 0.7006833893 0.5339558989 -12.1026559357 18.9366042752 -12.1026559357 18.9366042752
X Variable 1 -1.3265682657 2.9081572319 -0.4561542447 0.6792756863 -10.5816311856 7.9284946543 -10.5816311856 7.9284946543
X Variable 2 0.2158671587 0.3411538399 0.6327560574 0.5718314052 -0.8698376371 1.3015719544 -0.8698376371 1.3015719544
RESIDUAL OUTPUT
Observation Predicted Y Residuals Standard Residuals
1 1.6273062731 -1.6273062731 -1.1680490269
2 2.1808118081 -1.1808118081 -0.8475639198
3 1.6273062731 0.3726937269 0.2675123622
4 1.6273062731 1.3726937269 0.9852930567
5 4.7084870849 -0.7084870849 -0.5085383519
6 3.2287822878 1.7712177122 1.2713458797

Sheet6

0
0
0
0
0
0
X Variable 1
Residuals
X Variable 1 Residual Plot

Sheet7

0
0
0
0
0
0
X Variable 2
Residuals
X Variable 2 Residual Plot

Sheet8

0 0
1 0
2 0
3 0
4 0
5 0
Y
Predicted Y
X Variable 1
Y
X Variable 1 Line Fit Plot

Sheet10

0 0
1 0
2 0
3 0
4 0
5 0
Y
Predicted Y
X Variable 2
Y
X Variable 2 Line Fit Plot

Sheet1

SUMMARY OUTPUT
Regression Statistics
Multiple R 0.5735253764
R Square 0.3289313574
Adjusted R Square 0.1611641968
Standard Error 0.2410543825
Observations 6
ANOVA
df SS MS F Significance F
Regression 1 0.1139274524 0.1139274524 1.9606420955 0.2340371757
Residual 4 0.2324288613 0.0581072153
Total 5 0.3463563137
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 0.3358379483 0.1744623535 1.9249880653 0.1265429144 -0.1485482024 0.8202240991 -0.1485482024 0.8202240991
X Variable 1 0.0806854394 0.0576230189 1.4002293011 0.2340371757 -0.0793020406 0.2406729195 -0.0793020406 0.2406729195
RESIDUAL OUTPUT antilog(.33583795) = 2.1668954035
antilog(.08068544) = 1.2041634459
Observation Predicted Y Residuals Standard Residuals
1 0.3358379483 -0.0348079527 -0.1614427158
2 0.4165233878 0.2824466166 1.3100152506
3 0.4972088272 -0.1961788316 -0.9098967597
4 0.5778942667 -0.276864271 -1.2841237817
5 0.6585797061 0.1865183339 0.8650904192
6 0.7392651456 0.0388861048 0.1803575874

Sheet1

0
0
0
0
0
0
X Variable 1
Residuals
X Variable 1 Residual Plot

Sheet9

0.3010299957 0
0.6989700043 0
0.3010299957 0
0.3010299957 0
0.84509804 0
0.7781512504 0
Y
Predicted Y
X Variable 1
Y
X Variable 1 Line Fit Plot

Sheet11

SUMMARY OUTPUT
Regression Statistics
Multiple R 0.0377358491
R Square 0.0014239943
Adjusted R Square -0.3314346743
Standard Error 2.6564268809
Observations 5
ANOVA
df SS MS F Significance F
Regression 1 0.0301886792 0.0301886792 0.0042780749 0.9519646302
Residual 3 21.1698113208 7.0566037736
Total 4 21.2
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 4.2641509434 2.3927325591 1.7821260162 0.172748462 -3.3505990923 11.8789009791 -3.3505990923 11.8789009791
X Variable 1 0.0377358491 0.576939051 0.065406994 0.9519646302 -1.7983434246 1.8738151227 -1.7983434246 1.8738151227
RESIDUAL OUTPUT
Observation Predicted Y Residuals Standard Residuals
1 4.3396226415 0.6603773585 0.2870540488
2 4.4528301887 -2.4528301887 -1.0662007525
3 4.3396226415 -2.3396226415 -1.016991487
4 4.3396226415 2.6603773585 1.1564177393
5 4.5283018868 1.4716981132 0.6397204515

Sheet11

0
0
0
0
0
X Variable 1
Residuals
X Variable 1 Residual Plot

Sheet12

5 0
2 0
2 0
7 0
6 0
Y
Predicted Y
X Variable 1
Y
X Variable 1 Line Fit Plot

Sheet2

Sheet3

Year Sales 1st Coded Sales Sales^2 log(sales)
1994 2 0 2 4 0.3010299957
1995 5 2 1 5 25 0.6989700043
1996 2 5 2 2 4 0.3010299957
1997 2 2 3 2 4 0.3010299957
1998 7 2 4 7 49 0.84509804
1999 6 7 5 6 36 0.7781512504
0

Sheet3

0
0
0
0
0
0
Sales
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.3977058393
R Square 0.1581699346
Adjusted R Square -0.0101960784
Standard Error 1.4712939483
Observations 7
ANOVA
df SS MS F Significance F
Regression 1 2.0336134454 2.0336134454 0.9394409938 0.3769372438
Residual 5 10.8235294118 2.1647058824
Total 6 12.8571428571
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 1.2941176471 1.9868077019 0.6513552599 0.5435635839 -3.8131257969 6.401361091 -3.8131257969 6.401361091
X Variable 1 0.6470588235 0.667588751 0.9692476432 0.3769372438 -1.0690298893 2.3631475364 -1.0690298893 2.3631475364
RESIDUAL OUTPUT
Observation Predicted Y Residuals Standard Residuals
1 3.8823529412 -0.8823529412 -0.6569518078
2 3.2352941176 -1.2352941176 -0.9197325309
3 2.5882352941 0.4117647059 0.3065775103
4 3.2352941176 -1.2352941176 -0.9197325309
5 2.5882352941 -0.5882352941 -0.4379678719
6 2.5882352941 1.4117647059 1.0511228925
7 3.8823529412 2.1176470588 1.5766843387
0
0
0
0
0
0
0
X Variable 1
Residuals
X Variable 1 Residual Plot
3 0
2 0
3 0
2 0
2 0
4 0
6 0
Y
Predicted Y
X Variable 1
Y
X Variable 1 Line Fit Plot
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.6920231961
R Square 0.4788961039
Adjusted R Square 0.1314935065
Standard Error 1.4930394056
Observations 6
ANOVA
df SS MS F Significance F
Regression 2 6.1458333333 3.0729166667 1.3785046729 0.3761720123
Residual 3 6.6875 2.2291666667
Total 5 12.8333333333
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 3.5 3.501487779 0.9995751009 0.3911779502 -7.643307299 14.643307299 -7.643307299 14.643307299
X Variable 1 0.8125 0.834634401 0.9734801238 0.4021141357 -1.8436816575 3.4686816575 -1.8436816575 3.4686816575
X Variable 2 -0.9375 0.834634401 -1.1232462967 0.3431108215 -3.5936816575 1.7186816575 -3.5936816575 1.7186816575
RESIDUAL OUTPUT
Observation Predicted Y Residuals Standard Residuals
1 2.1875 -0.1875 -0.1621266379
2 2.3125 0.6875 0.5944643391
3 4.0625 -2.0625 -1.7833930173
4 2.3125 -0.3125 -0.2702110632
5 3.25 0.75 0.6485065518
6 4.875 1.125 0.9727598276
0
0
0
0
0
0
X Variable 1
Residuals
X Variable 1 Residual Plot
0
0
0
0
0
0
X Variable 2
Residuals
X Variable 2 Residual Plot
2 2.1875
3 2.3125
2 4.0625
2 2.3125
4 3.25
6 4.875
Y
Predicted Y
X Variable 1
Y
X Variable 1 Line Fit Plot
2 2.1875
3 2.3125
2 4.0625
2 2.3125
4 3.25
6 4.875
Y
Predicted Y
X Variable 2
Y
X Variable 2 Line Fit Plot
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.6477984695
R Square 0.4196428571
Adjusted R Square -1.3214285714
Standard Error 2.5495097568
Observations 5
ANOVA
df SS MS F Significance F
Regression 3 4.7 1.5666666667 0.241025641 0.8655519358
Residual 1 6.5 6.5
Total 4 11.2
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 2 13.2098448136 0.1514022328 0.9043408499 -165.8462736521 169.8462736521 -165.8462736521 169.8462736521
X Variable 1 1 2 0.5 0.7048327648 -24.4123006016 26.4123006016 -24.4123006016 26.4123006016
X Variable 2 -0.5 2.9580398915 -0.1690308509 0.8933992419 -38.0852994578 37.0852994578 -38.0852994578 37.0852994578
X Variable 3 0 1.7320508076 0 1 -22.0076978896 22.0076978896 -22.0076978896 22.0076978896
RESIDUAL OUTPUT
Observation Predicted Y Residuals Standard Residuals
1 2.5 0.5 0.3922322703
2 4 -2 -1.5689290811
3 2.5 -0.5 -0.3922322703
4 3 1 0.7844645406
5 5 1 0.7844645406
0
0
0
0
0
X Variable 1
Residuals
X Variable 1 Residual Plot
0
0
0
0
0
X Variable 2
Residuals
X Variable 2 Residual Plot
0
0
0
0
0
X Variable 3
Residuals
X Variable 3 Residual Plot
3 0
2 0
2 0
4 0
6 0
Y
Predicted Y
X Variable 1
Y
X Variable 1 Line Fit Plot
3 0
2 0
2 0
4 0
6 0
Y
Predicted Y
X Variable 2
Y
X Variable 2 Line Fit Plot
3 0
2 0
2 0
4 0
6 0
Y
Predicted Y
X Variable 3
Y
X Variable 3 Line Fit Plot
1 4
2 3 4
3 2 3 4
3 3 2 3 4
4 2 3 2 3
5 2 2 3 2
6 4 2 2 3
7 6 4 2 2

*

Evaluating Forecasts

*

112

Quantitative
Forecasting Steps

  • Select several forecasting methods
  • ‘Forecast’ the past
  • Evaluate forecasts
  • Select best method
  • Forecast the future
  • Monitor continuously forecast accuracy

*

113

Forecasting Guidelines

  • No pattern or direction in forecast error
  • ei = (Actual Yi - Forecast Yi)
  • Seen in plots of errors over time
  • Smallest forecast error
  • Measured by mean absolute deviation
  • Simplest model
  • Called principle of parsimony

*

114

Summary

  • Described what forecasting is
  • Explained time series & its components
  • Smoothed a data series
  • Moving average
  • Exponential smoothing
  • Forecasted using trend models

*

121

© Wiley 2010

*

Linear Trend Line

A time series technique that computes a forecast with trend by drawing a straight line through a set of data using this formula:

Y = a + bx where

Y = forecast for period X

X = the number of time periods from X = 0

A = value of y at X = 0 (Y intercept)

B = slope of the line

*

© Wiley 2010

*

Forecasting Trend

  • Basic forecasting models for trends compensate for the lagging that would otherwise occur
  • One model, trend-adjusted exponential smoothing uses a three step process
  • Step 1 - Smoothing the level of the series

  • Step 2 – Smoothing the trend

  • Forecast including the trend

*

© Wiley 2010

*

Forecasting Seasonality

  • Calculate the average demand per season
  • E.g.: average quarterly demand
  • Calculate a seasonal index for each season of each year:
  • Divide the actual demand of each season by the average demand per season for that year
  • Average the indexes by season
  • E.g.: take the average of all Spring indexes, then of all Summer indexes, ...

*

© Wiley 2010

*

Seasonality con’t

  • Forecast demand for the next year & divide by the number of seasons
  • Use regular forecasting method & divide by four for average quarterly demand
  • Multiply next year’s average seasonal demand by each average seasonal index
  • Result is a forecast of demand for each season of next year

*

© Wiley 2010

*

Seasonality problem: a university must develop forecasts for the next year’s quarterly enrollments. It has collected quarterly enrollments for the past two years. It has also forecast total enrollment for next year to be 90,000 students. What is the forecast for each quarter of next year?

Quarter Year 1 Seasonal Index Year 2 Seasonal Index Avg. Index Year3
Fall 24000 1.2 26000 1.238 1.22 27450
Winter 23000 22000
Spring 19000 19000
Summer 14000 17000
Total 80000 84000 90000
Average 20000 21000 22500

*

© Wiley 2010

*

Causal Models

  • Often, leading indicators can help to predict changes in future demand e.g. housing starts
  • Causal models establish a cause-and-effect relationship between independent and dependent variables
  • A common tool of causal modeling is linear regression:
  • Additional related variables may require multiple regression modeling

*

© Wiley 2010

*

Linear Regression

  • Identify dependent (y) and independent (x) variables
  • Solve for the slope of the line
  • Solve for the y intercept
  • Develop your equation for the trend line

Y=a + bX

*

© Wiley 2010

*

Linear Regression Problem: A maker of golf shirts has been tracking the relationship between sales and advertising dollars. Use linear regression to find out what sales might be if the company invested $53,000 in advertising next year.

Sales $ (Y) Adv.$ (X) XY X^2 Y^2
1 130 32 4160 2304 16,900
2 151 52 7852 2704 22,801
3 150 50 7500 2500 22,500
4 158 55 8690 3025 24964
5 153.85 53
Tot 589 189 28202 9253 87165
Avg 147.25 47.25

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© Wiley 2010

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Correlation Coefficient
How Good is the Fit?

  • Correlation coefficient (r) measures the direction and strength of the linear relationship between two variables. The closer the r value is to 1.0 the better the regression line fits the data points.
  • Coefficient of determination ( ) measures the amount of variation in the dependent variable about its mean that is explained by the regression line. Values of ( ) close to 1.0 are desirable.

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© Wiley 2010

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Multiple Regression

  • An extension of linear regression but:
  • Multiple regression develops a relationship between a dependent variable and multiple independent variables. The general formula is:

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© Wiley 2010

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Measuring Forecast Error

  • Forecasts are never perfect
  • Need to know how much we should rely on our chosen forecasting method
  • Measuring forecast error:

  • Note that over-forecasts = negative errors and under-forecasts = positive errors

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© Wiley 2010

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Measuring Forecasting Accuracy

  • Mean Absolute Deviation (MAD)
  • measures the total error in a forecast without regard to sign
  • Cumulative Forecast Error (CFE)
  • Measures any bias in the forecast

  • Mean Square Error (MSE)
  • Penalizes larger errors
  • Tracking Signal
  • Measures if your model is working

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© Wiley 2010

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Accuracy & Tracking Signal Problem: A company is comparing the accuracy of two forecasting methods. Forecasts using both methods are shown below along with the actual values for January through May. The company also uses a tracking signal with ±4 limits to decide when a forecast should be reviewed. Which forecasting method is best?

Month Actual sales Method A Method B
F’cast Error Cum. Error Tracking Signal F’cast Error Cum. Error Tracking Signal
Jan. 30 28 2 2 2 27 2 2 1
Feb. 26 25 1 3 3 25 1 3 1.5
March 32 32 0 3 3 29 3 6 3
April 29 30 -1 2 2 27 2 8 4
May 31 30 1 3 3 29 2 10 5
MAD 1 2
MSE 1.4 4.4

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© Wiley 2010

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Selecting the Right Forecasting Model

The amount & type of available data

Some methods require more data than others

Degree of accuracy required

Increasing accuracy means more data

Length of forecast horizon

Different models for 3 month vs. 10 years

Presence of data patterns

Lagging will occur when a forecasting model meant for a level pattern is applied with a trend

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© Wiley 2010

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Forecasting Software

  • Spreadsheets
  • Microsoft Excel, Quattro Pro, Lotus 1-2-3
  • Limited statistical analysis of forecast data
  • Statistical packages
  • SPSS, SAS, NCSS, Minitab
  • Forecasting plus statistical and graphics
  • Specialty forecasting packages
  • Forecast Master, Forecast Pro, Autobox, SCA

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© Wiley 2010

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Guidelines for Selecting Software

  • Does the package have the features you want?
  • What platform is the package available for?
  • How easy is the package to learn and use?
  • Is it possible to implement new methods?
  • Do you require interactive or repetitive forecasting?
  • Do you have any large data sets?
  • Is there local support and training available?
  • Does the package give the right answers?

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© Wiley 2010

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Other Forecasting Methods

  • Focus Forecasting
  • Developed by Bernie Smith
  • Relies on the use of simple rules
  • Test rules on past data and evaluate how they perform
  • Combining Forecasts
  • Combining two or more forecasting methods can improve accuracy

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Ethics in Social Science Research

Most people learn ethical norms at home, at school, in church, or in other social settings. Although most people acquire their sense of right and wrong during childhood, moral development occurs throughout life and human beings pass through different stages of growth as they mature.

Ethical norms are so ubiquitous that one might be tempted to regard them as simple commonsense. On the other hand, if morality were nothing more than commonsense, then why are there so many ethical disputes and issues in our society?

One plausible explanation of these disagreements is that all people recognize some common ethical norms but different individuals interpret, apply, and balance these norms in different ways in light of their own values and life experiences.



Quantitative Methods


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Ethics in Social Science Research

Most societies also have legal rules that govern behaviour, but ethical norms tend to be broader and more informal than laws. Although most societies use laws to enforce widely accepted moral standards and ethical and legal rules use similar concepts, it is important to remember that ethics and law are not the same.

An action may be legal but unethical or illegal but ethical. We can also use ethical concepts and principles to criticize, evaluate, propose, or interpret laws. Indeed, in the last century, many social reformers urged citizens to disobey laws in order to protest what they regarded as immoral or unjust laws. Peaceful civil disobedience is an ethical way of expressing political viewpoints.


Quantitative Methods
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Ethics in Social Science Research

Another way of defining 'ethics' focuses on the disciplines that study standards of conduct, such as philosophy, theology, law, psychology, or sociology. For example, a "medical ethicist" is someone who studies ethical standards in medicine. Finally, one may also define ethics as a method, procedure, or perspective for deciding how to act and for analyzing complex problems and issues.

For instance, in a complex issue like global warming, one may take an economic, ecological, political, or ethical perspective on the problem. While an economist might examine the cost and benefits of various policies related to global warming, an environmental ethicist could examine the ethical values and principles at stake in the issue.


Quantitative Methods


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Ethics in Social Science Research

Many different disciplines, institutions, and professions have norms for behaviour that suit their particular aims and goals. These norms also help members of the discipline to coordinate their actions or activities and to establish the public's trust of the discipline. For instance, ethical norms govern conduct in medicine, law, engineering, and business.

Ethical norms also serve the aims or goals of research and apply to people who conduct scientific research or other scholarly or creative activities, and there is a specialized discipline, research ethics, which studies these norms


Quantitative Methods


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Ethics in Social Science Research

There are several reasons why it is important to adhere to ethical norms in research. First, some of these norms promote the aims of research, such as knowledge, truth, and avoidance of error. For example, prohibitions against fabricating, falsifying, or misrepresenting research data promote the truth and avoid error.

Second, since research often involves a great deal of cooperation and coordination among many different people in different disciplines and institutions, many of these ethical standards promote the values that are essential to collaborative work, such as trust, accountability, mutual respect, and fairness. For example, many ethical norms in research, such as guidelines for authorship, copyright and patenting policies, data sharing policies, and confidentiality rules in peer review, are designed to protect intellectual property interests while encouraging collaboration.


Quantitative Methods


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Ethics in Social Science Research

Third, many of the ethical norms help to ensure that researchers can be held accountable to the public. For instance, Government policies on research misconduct, on conflicts of interest, on the human subjects protections, and on animal care and use are necessary in order to make sure that researchers who are funded by public money can be held accountable to the public.

Fourth, ethical norms in research also help to build public support for research. People more likely to fund research project if they can trust the quality and integrity of research.

Finally, many of the norms of research promote a variety of other important moral and social values, such as social responsibility, human rights, animal welfare, compliance with the law, and health and safety. Ethical lapses in research can significantly harm to human and animal subjects, students, and the public.


Quantitative Methods


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Ethics in Social Science Research

Codes and Policies for Research Ethics

Given the importance of ethics for the conduct of research, it should come as no surprise that many different professional associations, government agencies, and universities have adopted specific codes, rules, and policies relating to research ethics.

The following is a rough and general summary of some ethical principals that various codes address:

  • Honesty

Strive for honesty in all scientific communications. Honestly report data, results, methods and procedures, and publication status. Do not fabricate, falsify, or misrepresent data. Do not deceive colleagues, granting agencies, or the public.


Quantitative Methods


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Ethics in Social Science Research

Codes and Policies for Research Ethics

  • Objectivity

Strive to avoid bias in experimental design, data analysis, data interpretation, peer review, personnel decisions, grant writing, expert testimony, and other aspects of research where objectivity is expected or required. Avoid or minimize bias or self-deception. Disclose personal or financial interests that may affect research.

  • Integrity

Keep your promises and agreements; act with sincerity; strive for consistency of thought and action.


Quantitative Methods


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Ethics in Social Science Research

Codes and Policies for Research Ethics

  • Carefulness

Avoid careless errors and negligence; carefully and critically examine your own work and the work of your peers. Keep good records of research activities, such as data collection, research design, and correspondence with agencies or journals.

  • Openness

Share data, results, ideas, tools, resources. Be open to criticism and new ideas.

  • Respect for Intellectual Property

Honour patents, copyrights, and other forms of intellectual property. Do not use unpublished data, methods, or results without permission. Give credit where credit is due. Give proper acknowledgement or credit for all contributions to research. Never plagiarize.


Quantitative Methods
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Ethics in Social Science Research

Codes and Policies for Research Ethics

  • Respect for colleagues

Respect your colleagues and treat them fairly.

  • Social Responsibility

Strive to promote social good and prevent or mitigate social harms through research, public education, and advocacy.

  • Non-Discrimination

Avoid discrimination against colleagues or students on the basis of sex, race, ethnicity, or other factors that are not related to their scientific competence and integrity.


Quantitative Methods


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Ethics in Social Science Research

Codes and Policies for Research Ethics

  • Competence

Maintain and improve your own professional competence and expertise through lifelong education and learning; take steps to promote competence in science as a whole.

  • Confidentiality

Protect confidential communications, such as papers or grants submitted for publication, personnel records, trade or military secrets, and patient records.

  • Responsible Publication

Publish in order to advance research and scholarship, not to advance just your own career. Avoid wasteful and duplicative publication.


Quantitative Methods


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Ethics in Social Science Research

Codes and Policies for Research Ethics

  • Responsible Mentoring

Help to educate, mentor, and advise students. Promote their welfare and allow them to make their own decisions.

  • Respect for colleagues

Respect your colleagues and treat them fairly.

  • Legality

Know and obey relevant laws and institutional and governmental policies.


Quantitative Methods


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Ethics in Social Science Research

Codes and Policies for Research Ethics

  • Human Subjects Protection

When conducting research on human subjects, minimize harms and risks and maximize benefits; respect human dignity, privacy, and autonomy; take special precautions with vulnerable populations; and strive to distribute the benefits and burdens of research fairly.


Quantitative Methods


Source: * Adapted from Shamoo A and Resnik D. 2003. Responsible Conduct of Research (New York: Oxford University Press).

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Questions?

ANOVA

Good Luck


Quantitative Methods


Source: * Adapted from Shamoo A and Resnik D. 2003. Responsible Conduct of Research (New York: Oxford University Press).

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Source: https://www.hanze.nl/en/research/researchportal/centre-of-applied-research-and-innovation/art-society/lifelong-learning-in-music/knowledge-base/online-research-coach/pages/what-is-research.aspx?wbc_purpose=B

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Source: http://writingcenter.utah.edu/_docs/organization_693_1320713252.pdf

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Source: http://writingcenter.utah.edu/_docs/organization_693_1320713252.pdf

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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http://writingcenter.utah.edu/_docs/organization_693_1320713252.pdf

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May use books as a reference source: e.g. the definition of SSL.

BOUNDARIES, LIMITATIONS, CONCLUSIONS are the key ideas here

Maths – have to skip, in refereed publications, should be able to take it on trust that the referees have checked it, unless there are other reasons to suspect it.

Get other papers, do web search for terminology.

Professor John Swales, linguistics expert on the structure of scientific communication.

Do note that throughout the presentation, and within your coursework, you may hear terms like annotated bib, annotation, or lit review. [CLICK] Know that annotated bib and annotation are other ways to refer to elements in an annotated bibliography, and lit review is a shortened way to say literature review.

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Now that we have some vocabulary terms settled, let’s first begin with the term annotated bibliography. We at the Writing Center often receive questions from students on what this term means, so let’s break it down: According to Merriam-Webster’s Online Dictionary, the verb “annotate” means “to make or furnish critical or explanatory notes or comment.” The words to pick out of this definition are “critical, explanatory, and notes.” As for the term bibliography, this term is defined as “the history, identification, or description of writings or publications.” So, if we are to combine these terms together, we can determine that an annotated bibliography is a collection of explanatory, critical notes on a list of sources. You could also think of this term to refer to a reference list with a chunk of text below each entry that describes the nature of that source.

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Before we get into how to create an annotated bib, writers do need to understand the purpose of the assignment. Without knowing why you are writing an annotated bib, your assignment might not be fulfilling the expectations of your instructor. Overall, there can be many functions of an annotated bib. As a reader, you might want to seek out an annotated bibliography to learn about a specific topic. As a writer, however, creating a annotated bib will allow you to demonstrate the value (or lack of value) of a particular source and to help inform future researchers about a source or topic.

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One of the primary questions we at the Writing Center receive about annotated bibliographies is how to format them. Understanding the format of an annotated bib can be the first step in your prewriting process. Essentially, while the format can change depending your specific assignment, an annotated bibliography is formatted as a list of alphabetized reference entries (think of how a typical course paper reference list would look), with each entry followed by an annotation. There are typically no headings to separate the sources or within the annotations, and each annotation should be brief (anywhere from one to two pages). [CLICK] While these formatting requirements are typical for annotated bibs, be sure to ask your instructor about any alternative expectations for your specific assignment.

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Here is a visual representation of a portion of an annotated bibliography. [CLICK] You will notice the annotation begins with a reference citation, followed immediately by the first line of text. This reference citation should be in typical APA formatting (for example, double spaced, using a hanging indent, and so forth). This first annotation is concise and is only about one page in length. [CLICK] Typically, you will want subsequent annotations to begin immediately following the previous one. Note that there are no spaces or headings between the end of this first annotation and the new reference entry of the next.

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Now that we have the overall formatting down, let’s get into the nitty gritty details about what an “annotation” is and what is entailed within this text that follows the reference entry. As you begin to construct your annotation, you will focus on three elements: a summary of the source, a critical analysis of the source, and an explanation of how that source applies to your particular topic. To ensure that you fully develop each part of the annotation, instructors will usually ask for each element to be in its own paragraph.

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Let’s start with the summary element. After reading a source and determining how it would fit into your research or topic, your natural instinct is to, typically, summarize the source. When creating a summary paragraph for an annotation, some questions to answer and include within your summary paragraph could be:

What is the topic of the source?

What actions did the author perform within the study and why?

What were the methods of the author?

What was the theoretical basis for the study?

What were the conclusions of the study?

These questions hit at all key elements of a study and give your reader a high level view of that source.

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Just like answering those questions, some strategies to creating a summary paragraph are to think of it like an abstract, which introduces the topic, development, and conclusions of an article. However, you will want your phrasing of the summary to be in the past tense per APA 3.06 preferences, using phrases like “The authors found…” or “stated.” Do note, though, that a summary paragraph should not be the exact abstract of the article. Avoid the temptation to copy/paste the abstract information into an annotation and instead summarize the source in your own words.

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Let’s take a look at this sample summary paragraph of an annotation. Note: these elements, like the reference entry and text, will be double spaced for the final paper. I won’t read this entire paragraph to you, but I’ll quickly highlight what the author is doing here. [CLICK] First, this opening line of the annotation immediately discuss the topic and purpose of the article. The reader does not have to dig through a lot of background information to get to the “meat” of the summary. [CLICK] Also, this writer briefly refers to the method, data collection, and analysis of the material that these authors included in the article. This student included all of these elements into one or two sentences, which allows the reader to quickly move into the conclusions of the article. [CLICK] These conclusions are also mentioned in this summary paragraph at the end, and this student takes particular care to mention the results of the study and their overall implication to the study.

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The next element to a successful annotation is the critique or analysis portion. This aspect is often neglected by students, but this paragraph can be the most important to you as the researcher and to your reader. To help create this paragraph, try answering the following questions:

What are the strengths and weaknesses of the article?

Methodology, language choices, organization, level of detail

What, if any, information is missing?

Is the article scholarly or generalizable? Why or why not?

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There are also some ways to approach your critique outside of these questions, and the best way to ensure you don’t forget to include this part to the annotation is to focus on the strengths. Highlighting these strengths will help an outside reader understand the impact and influence this source has on your research field, and this approach can also help you remember to revisit this source as you develop your own study and want to know what works best. However, students often will feel the need to be “nice” to the author in the analysis paragraph just because the article has been peer reviewed or published. Remember, though, that the majority of published authors in the social science field were once students, and just like a capstone or final project for a course, there can also be room for revision or areas for improvement. If you wished that the author had place more emphasis on a particular result or included more tables in the article to aid readability, feel free to refer to these missing elements and explain how they could have improved the source. That way, your reader will know that you not only engaged with the topic of the article but also the method and mode of the written aspect.

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Using this approach, here’s an example analysis paragraph in an annotated bibliography. [CLICK] The student here notes an aspect of the article where information was lacking. Similarly, [CLICK] at the end of the paragraph, the student mentions what could have been added or improved in the study but is being constructive in the approach.

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The final element to an annotation is the application portion. This element can be just as tricky as the analysis part, as it requires you as the writer to view a source not just by methods or written quality but as a piece of literature in the broader field of research. To do so, you will want to answer these types of questions:

Does this article fill a gap in literature?

How would you be able to apply this method to your area of focus?

Is the article universal?

Don’t feel any pressure to “get it right,” though. Remember this annotation is your interpretation of the applicability of a source, so as long as you have support to back up your claims, your reader will understand your rationale for this annotation element.

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When creating the application paragraph, which is often the shortest paragraph in an annotation, consider how you would justify using (or not using) a particular source for your research. Focusing on the unique elements of a source, such as population or method, will help you collect a series of diverse sources on your topic. Similarly, though, if a source is too unique or too narrow, include these limitations in your text. In addition, this application portion should hint at how this source would justify the need for your own research, such as if an author mentioned how to build upon a study or where the field as a whole needs more data.

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Let’s take a look at this example application paragraph. You may notice that, when compared to the previous two paragraphs, it is the shortest. [CLICK] here, the student mentions how this article and approach is unique within the literature. [CLICK] Also, the student ends with a discussion on the universal nature of the source and why (or why not) it would be beneficial to the student’s own research or topic. [CLICK] These three elements, summary, critique, and application, create an “annotation.”

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When constructing these annotations, do know that there are some unique characteristics of which to be aware, and these characteristics do differ from typically coursework expectations. First, there is no need to cite your source within your annotation; the reference entry that begins the annotation will let your reader know to what source you are referring (which, in essence, is what a citation is supposed to do). Also, you will want to avoid including any outside source citations. Each annotation should purely focus on what is housed within that source, and comparing/contrasting sources should be saved for a literature review (which we’ll get to soon). Second, direct quotes should not be present in an annotation. This text is intended for you as the author to demonstrate the value of a source, so paraphrasing is key. You will also want avoid references to yourself or the first person (like I, my, or mine). An annotation should be objective and, just like the guideline to only paraphrase, should just focus on the validity of the source in regard to the overall field. And lastly, you will typically not have to include a reference list for an annotated bibliography, as each source has already been included in its APA format. However, if an annotated bibliography is a part of a longer document, there may be different requirements for that assignment, so contact your instructor for his or her preferences.

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Now that we know the purpose and format of an annotated bibliography, how does this assignment relate to a literature review? [CLICK] Well, an annotated bibliography is often the first step to creating a literature review, as it will allow you to collect sources and determine their value to your research. [CLICK] In a lit review, however, you will use these sources together to create a foundation or justification for your research.

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Textbooks, essays, newspapers, novels, magazines, articles, cookbooks, songs, political speeches, advertisements, pictures [even artwork] -- in fact, the contents of virtually any type of communication can be analyzed.

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Textbooks, essays, newspapers, novels, magazines, articles, cookbooks, songs, political speeches, advertisements, pictures [even artwork] -- in fact, the contents of virtually any type of communication can be analyzed.

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Textbooks, essays, newspapers, novels, magazines, articles, cookbooks, songs, political speeches, advertisements, pictures [even artwork] -- in fact, the contents of virtually any type of communication can be analyzed.

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Textbooks, essays, newspapers, novels, magazines, articles, cookbooks, songs, political speeches, advertisements, pictures [even artwork] -- in fact, the contents of virtually any type of communication can be analyzed.

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Textbooks, essays, newspapers, novels, magazines, articles, cookbooks, songs, political speeches, advertisements, pictures [even artwork] -- in fact, the contents of virtually any type of communication can be analyzed.

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Source: http://classroom.synonym.com/content-analysis-2670.html

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Use Loret’s Coding Scheme as an Example; also, MySpace Coding.

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 *Assumes  a 95% level of confidence 

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Dr. G. Johnson, www.researchdemystified.org

Ch 10 Sampling more size and error

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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P = Probability

this value tells us the probability that the observed result was obtained by chance

That there is no difference between the two groups

Each test result (e.g. t value) is associated with a particular p value

α level is set a priori

This is basically an acceptance level

Usually this is set to 0.05

But as I understand, α levels are usually much lower than this in fMRI

If p < α level then we reject the null hypothesis and accept the experimental hypothesis

- concluding that we are 95% certain that our experimental effect is genuine

If however, p > α level then we reject the experimental hypothesis and accept the null hypothesis

- that there was no sig diff in brain activation levels between the two conditions

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Dr. G. Johnson, www.researchdemystified.org

Ch 10 Sampling more size and error

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Dr. G. Johnson, www.researchdemystified.org

Ch 10 Sampling more size and error

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Central Limit Theorem:

The sample avenge is approximately normally distributed.

68% of the values are within 1 standard deviation of the mean.

95% of the values are within 2 standard deviation of the mean.

 *Assumes  a 95% level of confidence 

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Dr. G. Johnson, www.researchdemystified.org

Ch 10 Sampling more size and error

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Dr. G. Johnson, www.researchdemystified.org

Ch 10 Sampling more size and error

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But recall Creswell piece…’qualitative approaches’ are not defined simply by methods.

Also ‘knowledge claims’ and ‘strategies of inquiry’

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Population generalizability is but one way of talking about and thinking about generalizability. We can also think about generalizing to theory. Abstractions can be drawn from small samples, from the atypical to build, refine or critique theory.

Some research is about identifying what is typical, some research is not.

Interview transcripts, ethnographic field notes are data. Data does not equal numbers.

Lacks rigor? How are we defining rigor?

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We should evaluate qualitative research by criteria that match with these points of emphasis these priorities.

Not hold it to criteria from other research traditions.

So let’s talk about these priorities, what they demand, and what trade-offs are involved.

Trade offs of ‘naturalistic observation’:  

  • lack of control over variables (as you would get in experimentation), (2) to get that close you have to be there which means you as a researcher can potentially disrupt, distort (sometimes that is actually very illuminating – people will explain the taken for granted to a stranger an outsider).

Trade offs of dealing with ‘subjective meanings of research subjects’

unavoidably face the limits of human communication and of ever knowing what is in someone’s mind.…also the flexibility of techniques required to invite that kind of self-expression creates some difficulty in doing comparisons – apples to oranges.

Trade offs of doing ‘inductive analysis’

not oriented to establishing causality, not well suited to generating precise measures of magnitude or distribution of a phenomenon

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If we strip it all down, this is one of the ways in terms of an orderly sequence - we are taught (and teach others) to do user research in the traditional, positivist framework.

The reality should not prevent us from having a logic to our work, especially when we formulate our HYPOTHESES.

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Quantitative is less sequential, linear, and orderly than one might presume.

Likewise Qualitative Research has an underlying structure guiding such work…not sequential, but iterative

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On the qualitative side:

  • Embraces complexity, contradiction – goes along with precision

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Source: https://www.google.com.gh/?gws_rd=cr,ssl&ei=Kt8VVKufLcfiaIb2gegI#q=Historical+Studies+ppt

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What is the difference between direct observation and participant observation

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What is quantitative data? What is qualitative data?

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Your concept map is the theoretical proposition for your case study

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  • (they are generalizable to theoretical propositions and not to populations or universes.)
  • (do not necessarily have to be long, as one could do a case study without ever having to leave the library or telephone.)

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Textbooks, essays, newspapers, novels, magazines, articles, cookbooks, songs, political speeches, advertisements, pictures [even artwork] -- in fact, the contents of virtually any type of communication can be analyzed.

*

*

*

Textbooks, essays, newspapers, novels, magazines, articles, cookbooks, songs, political speeches, advertisements, pictures [even artwork] -- in fact, the contents of virtually any type of communication can be analyzed.

*

Textbooks, essays, newspapers, novels, magazines, articles, cookbooks, songs, political speeches, advertisements, pictures [even artwork] -- in fact, the contents of virtually any type of communication can be analyzed.

*

Textbooks, essays, newspapers, novels, magazines, articles, cookbooks, songs, political speeches, advertisements, pictures [even artwork] -- in fact, the contents of virtually any type of communication can be analyzed.

*

Textbooks, essays, newspapers, novels, magazines, articles, cookbooks, songs, political speeches, advertisements, pictures [even artwork] -- in fact, the contents of virtually any type of communication can be analyzed.

*

Source: http://classroom.synonym.com/content-analysis-2670.html

*

Not a mechanical count of words; validity-established coding scale, automated on a computer.

*

Use Loret’s Coding Scheme as an Example; also, MySpace Coding.

*

*

*

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

*

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

*

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

*

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

*

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

*

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

*

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

*

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

*

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

*

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

*

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

*

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

*

Source: https://www.google.co.uk/search?q=greek+letters&newwindow=1&rlz=1C1SKPM_enGH499GH505&espv=210&es_sm=122&tbm=isch&tbo=u&source=univ&sa=X&ei=dboiU73NBKuBywOGzoLoCw&ved=0CDgQsAQ&biw=1316&bih=615#facrc=_&imgdii=_&imgrc=upeV0njO2SuPoM%253A%3B8NGcq9zXZ9jWdM%3Bhttp%253A%252F%252F0.tqn.com%252Fd%252Fgogreece%252F1%252F0%252Fu%252Fn%252FGreek-Alphabet-Chart-Letters.JPG%3Bhttp%253A%252F%252Fgogreece.about.com%252Fod%252Fgreeklanguage2%252Fss%252Fgreekalphabet_9.htm%3B345%3B350

*

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Q

Explain that by genuine we mean that the observed effect was caused by a true effect present within the whole population

A

*

P = Probability

this value tells us the probability that the observed result was obtained by chance

That there is no difference between the two groups

Each test result (e.g. t value) is associated with a particular p value

α level is set a priori

This is basically an acceptance level

Usually this is set to 0.05

But as I understand, α levels are usually much lower than this in fMRI

If p < α level then we reject the null hypothesis and accept the experimental hypothesis

- concluding that we are 95% certain that our experimental effect is genuine

If however, p > α level then we reject the experimental hypothesis and accept the null hypothesis

- that there was no sig diff in brain activation levels between the two conditions

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Source: James A. Davis, Elementary Survey Analysis, Prentice- Hall, Inc., Englewood Cliffs, New Jersey, 1971. p.49.

*

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

*

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

*

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

*

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

*

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

*

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

*

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

*

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

*

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

*

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

*

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

*

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

*

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

*

ethnography (simplest, clearest definition) – is the study of culture through the researcher’s immersion in that culture for a lengthy period of time.

ethno [nation] + graphy [writing] – writing a nation – does that illuminate the matter a bit? Maybe a little bit.

nation = defn 4 from the OED - “an aggregation of persons of the same ethnic family, often speaking the same language or cognate languages.”

Writing (description and argument) as a critical aspect (not just the transparent recording of findings).

If you are doing naturalistic observation are you doing ethnography? Not necessarily, but some observation and immersion is necessary but not sufficient.

*

*

Extensive use of unaltered data

Interpretation and meaning (wink vs. blink)

*

*

It is to be noted that these components exist in both formal research and action research. The goals and the process for identifying the research problem are the two components in action research that vary a bit from “formal research.” In general, they are similar. In action research, though, the goals are more focused on problem solving and the enhancement of professional practice. The goals of “formal research” lean more toward contributing to the body of knowledge in the field, in addition to contributing to the enhancement of practice.

Both “formal research” and Action Research use quantitative and qualitative designs and data collection and analysis strategies. It is important for the Action Researcher to understand both families of research in order to conduct Action Research appropriately, and to understand and be able to analysis and apply the literature reviewed.

*

These skills are needed in both “formal research” and action research.

*

These goals are specific to action research, but can also be applied as “sub goals” to “formal research.”

*

This process if specific to action research, but can be helpful in “formal research,” as well.

*

This is the step of clarifying your area of focus. When it comes to the “Why” of the situation, you will likely be trying out a few hypotheses about the situation. This is where reviewing the literature becomes absolutely critical. This is where you may find potential promising practices that may correct the problem you are addressing.

*

The researcher first assesses the existing situation. Through discussion and negotiation, one can narrow the focus of the research to the salient elements to be studied. Opportunities and resources for data collection and analysis should be examined, as should potential limitations in the environment. The result of these activities should be the concrete identification of what is to be the focus of the action research.

*

It’s true that all research requires the foundation of prior research. Research often suggests theory, which can then be tested for its relevance to reality. The more one knows about the area of focus, the more precise will be the action research to be conducted.

*

The literature reviewed and the definition of the area of focus should help the researcher determine what data is to be collected. In Action Research, there are always multiple sources of data, multiple kinds of data, and multiple strategies for collecting data (triangulation).

*

These are the specific steps that you would take to plan out your action research.

*

Validity in quantitative research refers to accuracy of measurement and ability to generalize results. Of prime consideration in qualitative research is accuracy of measurement. New language – trustworthiness and understanding – is more applicable and appropriate to qualitative research. Since Action Research uses qualitative designs and strategies moreso than quantitative, looking at the validity of Action Research in terms of the trustworthiness of the data and understanding, makes more sense. There are several major theorists whose concepts of validity applied to qualitative and Action Research are important to consider. Our focus will be on Action Research.

*

Applying language like trustworthiness and understanding to the validity of Action Research provides us the opportunity to make sure that our work meets professional standards. Anderson, Herr and Nihlen have offered these criteria as a systematic way to assess the quality of Action Research.

Democratic validity: Make sure “the problems emerge from a particular context and solutions are appropriate to that context” (Cunningham, 1983, p. 30). One way to do so is to involve teachers and administrators in a collaborative effort with subjects. Collaboration is essential to Action Research.

Outcome validity: the study can be considered valid if the results lead to the research learning something that can be applied to the subsequent research cycle.

Process validity: be vigilant in reflecting on the suitability of data collection strategies and modify the strategies if the data is not addressing the research questions.

Catalytic validity: very simply, the results should be a catalyst to taking some action to resolve the original problem.

Dialogic validity: more collaboration! Seeking the input of colleagues and peers establishes how “good” the research is (similar to peer review in traditional publications).

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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1

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2

As a result of this class, you will be able to...

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6

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15

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16

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21

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24

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25

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33

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35

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37

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53

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57

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58

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63

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81

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82

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86

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88

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89

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90

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91

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92

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93

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95

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96

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101

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104

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105

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112

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113

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114

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121

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Source: * Adapted from Shamoo A and Resnik D. 2003. Responsible Conduct of Research (New York: Oxford University Press).

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Source: * Adapted from Shamoo A and Resnik D. 2003. Responsible Conduct of Research (New York: Oxford University Press).

*

The "Population Standard Deviation":

The "Sample Standard Deviation":

Kappa Statistic Strength of

Agreement

<0.00 Poor

0.00 0.20

Slight

0.21 0.40

Fair

0.41 0.60

Moderate

0.61 0.80

Substantial

0.81 1.00

Almost Perfect

x

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Menu/Tools/DataAnalysis/AnovaSingleFactor

Anova: Single Factor

SUMMARY

Groups

Count

Sum

Average

Variance

0 nematode

4

42.6

10.65

4.21667

1000 nematodes

4

41.7

10.425

2.20917

5000 nematodes

4

22.4

5.6

1.54667

10000 nematodes

4

21.8

5.45

3.13667

ANOVA

Source of Variation

SS

df

MS

F

P-value

F crit

Between Groups

100.647

3

33.549

12.0797

0.00062

3.4902996

Within Groups

33.3275

12

2.77729

Total

133.974

15

ANOVA

SeedlingLength

100.647

3

33.549

12.080

.001

33.328

12

2.777

133.974

15

Between Groups

Within Groups

Total

Sum of

Squares

df

Mean Square

F

Sig.

ANOVA

Source of Variation

SS

df

MS

F

P-value

F crit

Between Groups

101

3

33.5

12.08

0.00062

3.4903

Within Groups

33.3

12

2.78

Total

134

15

Simple Linear Regression

0

2

4

6

8

10

12

12345678910

X

Y

2

R

)

1

(

1

2

2

2

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k

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k

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4

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5.2

5.4

5.8

6.6

5

6.3

6.4

6.6

7.0

7.7

4

9.0

9.1

9.3

9.6

10.1

3

19.3

19.3

19.2

19.0

18.5

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230.2

224.6

215.7

199.5

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TypeCharacteristicsStrengthsWeaknesses

Executive

opinion

A group of managers

meet & come up with

a forecast

Good for strategic or

new-product

forecasting

One person's opinion

can dominate the

forecast

Market

research

Uses surveys &

interviews to identify

customer preferences

Good determinant of

customer preferences

It can be difficult to

develop a good

questionnaire

Delphi

method

Seeks to develop a

consensus among a

group of experts

Excellent for

forecasting long-term

product demand,

technological

changes, and

Time consuming to

develop

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2

3

4

5

6

7

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1993

1994

1995

1996

1997

1998

1999

2000

Coefficients

Intercept

2.14285714

X Variable 1

0.74285714

2

2

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0

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Coefficients

Intercept

2.85714286

X Variable 1

-0.3285714

X Variable 2

0.21428571

Coefficients

Intercept

0.33583795

X Variable 1

0.08068544

i

X

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09/83

07/86

05/89

03/92

01/95

Month/Year

0

2

4

6

8

10

12

Number of Passengers

(X 1000)

Intra-Campus Bus Passengers

Data collected by Coop Student (10/6/95)

0

5

10

15

20

25

Lag

-1

-0.5

0

0.5

1

±

2

s

Intra-Campus Bus

Passengers

(Auto Correlation

Function

Plot

Coefficients

Intercept

3.5

X Variable 1

0.8125

X Variable 2

-0.9375

2

1

9375

8125

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3

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-

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+

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X

n

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153.85

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1.15

92.9

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92.9

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a

Y

92.9

a

47.25

1.15

147.25

X

b

Y

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1.15

47.25

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9253

147.25

47.25

4

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.982

r

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589

87,165

4

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(189)

-

4(9253)

589

189

28,202

4

r

Y

Y

n

*

X

X

n

Y

X

XY

n

r

2

2

2

2

2

2

2

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å

å

å

å

å

2

r

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Research Proposal__guide __Health__01__.pdf

Preliminary Title of Research Proposal

STUDENTS NAME

STUDENTS ID

Supervisor’s Name

Research proposal for the Master’s Degree in XXXXXXXXXXXXXXXX (XXXX)

at the Ghana Institute of Management and Public Administration (GIMPA): GIMPA

SCHOOL OF PUBLIC SERVICE AND GOVERNANCE (GSPSG)

[Date of Submission]

Table of Content

Pagination: Insert your heading and sub-headings according to their respective page/s. Page

numbers should be well aligned.

LIST OF TABLES

Table 1

Table 2

Table 3

Table 4

Table 5

Table 6

Table 7

Table 8

LIST OF FIGURES

Figure 1

Figure 2

Figure 3

Figure 4

Abstract

Note: Should be written at the end of your research and analysis.

Introduction

In Ghana, prevalence of anemia is high. There have been several strategies to combat anemia in

pregnancy in Ghana, but the gains have not been remarkable. This study looked at the prevalence

and factors associated with anemia in pregnancy as well as the knowledge of pregnant women

about anemia in pregnancy in Sekondi Takoradi Metropolis (STM), Ghana.

Method

Facility - based cross-sectional study was conducted. Pregnant women who met the inclusion

criteria and consented to, were systematically selected from three hospitals in the STM. They had

their antenatal cards reviewed and were made to answer interviewer administered questionnaire.

A total of 342 participants were proportionately selected from the three hospitals based on their

ante natal clinic attendance.

Results

1. Prevalence rate of anemia was high among pregnant women in the STM. The prevalence rate

for anemia in pregnancy was 69%. The highest Hb was 13.0 g/dL whilst the minimum was 7.2

g/dL. The mean Hb was 10.28g/dL and the Standard deviation was 1.23.

2. The risk factors for anemia included taking in less iron-rich food; low income status; low

educational level; malaria infection; and worm infestation.

3. The knowledge level of pregnant women at the STM about anemia in pregnancy was found to

be generally inadequate.

4. The severity of anemia prevalence turned to improve over the course of pregnancy. And the

higher the knowledge about anemia, the less likely one was to become anemic.

Conclusion and Recommendation

The prevalence of Anemia in pregnancy was 69% which was very high. The knowledge of

pregnant women about anemia in pregnancy was not in-depth. Poor intake of iron rich meals as

well as relatively low-income levels and educational levels were associated with anemia in

pregnancy in the STM.

1. There is the need to focus on preconception prevention of anemia.

2. There is the need to encourage early start of ante-natal clinic and Provision of iron

supplement to boost iron intakes among pregnant women.

3. There is the need for the STM Assembly to set-up a support scheme for low income

earners who become pregnant.

4. Awareness about anemia among pregnant women and their possible causes and

prevention should be intensified through radio, social media, and education during

antenatal clinics.

5. Education of girls should be encouraged and enforced.

CHAPTER ONE

INTRODUCTION

1.0 Introduction

Describe and give an introductory overview and organisational context of the study. Indicate the

proposed topic of the research – what is the broad issue to be investigated?

1.1 Historical Development/Background of the study

Give reasons for selecting the particular problem – the rational for the study. Give an overview

of the subject area. By way of introduction, this reading section of the existing literature should

take the form of an abstract of the general subject or study area and identify the discipline(s)

within which it falls. From this analysis the problem or disorder you wish to research will

emerge and constitutes the reason or condition which necessitates the research.

1.2 Statement of the Research Problem

Formulate the proposed problem statement in paragraph. From the overview of the subject area

follows the research problem, that is you have to identify the possible cause(s) of the disorder.

This section states the problem that you are exploring. In the nutshell, this is where you explain

the research problem, question and aim.

1.3 Research Questions

Considering the problem, what are the questions that has to be answered? The research question

is specific, concise, and clear. The research question can be expanded upon by stating sub-

questions.

Note: The difference between the research problem and research question is that the problem is broader, while the

research question represents the “one question that you will answer at the end of your dissertation”.

1.4 Research Hypotheses/Propositions

Statements about the problem, offered for consideration or acceptance. Testable expectations

about the research questions, logically derived from the propositions, theory and/or observations.

Indicate the relative weight of the following types of research that you propose to undertake:

▪ Theory building research ▪ Theory testing research ▪ Theory application research

Note: 1.4 is a must if you intend to engage in quantitative research

1.5 Study Assumption/s

This is a social science study research with three philosophical reasoning and assumptions,

which form an integral part of my scientific learning’s and explanations, based on the saying

“The free, unhampered exchange of ideas and scientific conclusions is necessary for the sound

development of science, as it is in all spheres of cultural life” (Albert Einstein, 1952).

Note: 1.5 is a must if you intend to engage in quantitative research

1.6 Research Goal and Objectives

Goal

This is where you generally explain the research broad goal. You have to describe the research

broad goal as it relates to solving the uncertainty or burning question you are interested in. It

should explicitly hint towards the contribution you want to make with the intended study.

Objectives

What would you achieve by answering these questions? Break down the broad research goals

into manageable parts.

1.7 Research Methodology/Strategy

Present the proposed approach/paradigm and strategy for performing the research. Describe the

methodology (qualitative/quantitative and/or a mixed methodology) of study and/or research

design including the method/s to be employed.

Briefly, indicate the proposed research instrument/s (questionnaire, case study, and interview)

and methods of data collection and analysis. Give some justification of why the methods are

proposed.

1.8 Ethics Committee Approval

All research involving interaction with people must be submitted to the Ethics Committee of the

School for approval. Application or obtained letter of approval from the Dean through the

Secretariat.

1.9 Significance and Justification of the Study

State and provide a justification and/or significance of the research in terms:

▪ Theory/Knowledge

▪ Methodology

▪ Policy application _formulation and problem solving

1.10 Scope of the Study

Provide the breadth and depth of the research.

1.11 Limitations of the study

Anticipated problems and difficulties that may hinder this study include quality time needed for

effective field work and analysis and financial constraints in addition to the researcher and

questionnaire administrators’ biases. Indicate how you intend to overcome anticipated

challenges.

1.12 Structure and Organisation of the study

The study is presented in five interrelated chapters. The first chapter dwells on the introduction

and background of the study, followed by the statement of the problem, research goal and

objectives, research questions and organisation of the study. The next chapter two presents the

review of relevant literature on the subject matter under consideration. The research design and

methods of data collection and collation are spelt out in chapter three. Chapter four presents

analysis, discussions and interpretation of the results of the study. Finally, chapter five covers

summary of the major findings, conclusion and recommendations.

1.13 Conclusion of Introductory session

A concluding statement on the feasibility of completing the study as proposed.

Provide work schedule.

Use this schedule template to help you to plan your dissertation writing process. You may find

the following tips helpful:

• It is best to start with the end date (i.e. submission) and work backwards.

• Plan to submit your dissertation at least three weeks before the final deadline to give you some protection against delays caused by unexpected problems.

• Include in the schedule any other major commitments you may have during the dissertation writing period (e.g. examination revision).

• Once you have drafted your schedule, think about when would be the best times for you to meet with your supervisor and insert them into the schedule.

Plan: Indicative Dissertation Schedule

Stage of the dissertation writing

process

Number of

days/weeks

needed

Start date End date

STAGE ONE: Reading and research

a) Seek to identify an original,

manageable topic

b) Reading and research into chosen

topic

STAGE TWO: The detailed plan

a) Construct a detailed plan of the

dissertation

STAGE THREE: Initial writing

a) Draft the various sections of the

dissertation

b) Undertake additional research

where necessary

STAGE FOUR: The first draft

a) Compile and collate sections into

first draft of dissertation

b) check the flow of the dissertation

c) Check the length of the

dissertation

d) Undertake any additional editing

and research

STAGE FIVE: Final draft

a) Check for errors

b) Prepare for submission

c) Final proof-read (by a friend or

yourself) and final editing

d) Compile bibliography

e) Get the dissertation bound

f) Submit your dissertation

CHAPTER TWO

LITERATURE REVIEW, CONCEPTUAL AND THEORETICAL

FRAMEWORK

2.0 Introduction

Provide an introductory write up on the intention of this chapter.

It is important to have a solid foundation within the empirical literature in order to build a

successful research plan.

The literature review consists of 2 parts:

• the process

• the product.

The process is the literature search. A literature search is “a systematic and through search of all

types of published literature in order to identify as many items as possible that are relevant to a

particular topic” (Gash, 2000, p.1). If you are writing a dissertation or a thesis, then you should

expect to review between a 1,000 to 2,000 articles. In the product, you may only include 15 to 20

% of what you review. The product is the written document that is a coherent argument that leads

to a proposed study written from your perspective. It is a written synthesis of the literature

arranged around themes from your critical perspective. One of the most informative definitions

of a literature review is one that Ridley (2008) stated. The literature review is …” where there is

extensive reference to related research and theory in your field; it is where connections are made

between source texts you draw on and where you position yourself and your research among

other sources. It is the opportunity to engage in a written dialogue with researchers in your area

while at the same time showing that you have engaged with, understood and responded to the

relevant body of knowledge underpinning your research. The literature review is where you

identify theories and previous research which influenced your choice of research topic and the

methodology you are choosing to adopt. You can use the literature to support your identification

of a problem to research or illustrate that there is a gap in previous research that needs to be

filled. The literature review, therefore, serves as the driving force and the jumping off point for

your own research investigation” (Ridley, 2008, p.2).

When constructing a literature review, you want to ensure that it does the following:

• Provides context of the study and clarifies the relationship between the proposed research and

previous research, both empirical and theoretical

• Show how the proposed study is unique from previous research • Convince the reader that your

study is timely and worthwhile

• Demonstrate your critical ability as a scholar, not your knowledge of others’ works (e.g. “Jones

says…” “Anderson states…”). Formulate an argument from YOUR PERSPECTIVE.

• Make assertions and convince reader of their legitimacy by providing logical and empirical

support.

Your literature review should logically lead to your research problem, purpose, and questions,

which in turn leads to the identification of your research approach and design.

Topical Discussion: Theoretical or Conceptual Framework

Also, guiding your study is you theoretical or conceptual framework. According to Maxwell

(2005), “the point is not to summarize what has already been done in the field. Instead, it is to

ground your proposed study in the relevant previous work, and to give the reader a clear sense of

your theoretical approach to the phenomena that you propose to study” (p. 123) Maxwell (2005)

says that your conceptual or theoretical framework should serve two purposes: 1. Shows how

your research fits into what is already known (relationship to existing theory and research) 2.

Shows how your research makes a contribution on the topic to the field (its intellectual goals).

It also informs your research questions and methodology and helps you justify your research

problem (shows why your research is important). “In quantitative studies, one uses theory

deductively and places it toward the beginning of the plan for a study. The objective is to test or

verify theory. One thus begins the study advancing a theory, collects data to test it, and reflects

on whether the theory was confirmed or disconfirmed by the results in the study. The theory

becomes a framework for the entire study, an organizing model for the research questions or

hypotheses for the data collection procedure” (Creswell, 1994, pp. 87-88).

References

Creswell, J. W. (1994). Research design: Qualitative and quantitative approaches. Thousand

Oaks, CA: SAGE.

Gash, S. (2000) Effective literature searching for research. Aldershot: Gower.

Maxwell, J. A. (2005). Qualitative research design: An interactive approach (2nd ed.). Thousand

Oaks, CA: Sage Publications.

Ridley, D. (2008). The literature review: A step-by-step guide for students. Thousand Oaks, CA:

SAGE.

CHAPTER THREE

RESEARCH DESIGN AND METHODOLOGY

3.0 Introduction

Provide an introductory write up on the intention of this chapter.

3.1 Research Design and Approach

Note:

Research design `deals with a logical problem and not a logistical problem' (Yin, 1989: p. 29).

Before a builder or architect can develop a work plan or order materials they must first establish

the type of building required, its uses and the needs of the occupants. The work plan ¯flows from

this. Similarly, in social research the issues of sampling, method of data collection (e.g.

questionnaire, observation, document analysis), design of questions are all subsidiary to the

matter of `What evidence do I need to collect?' Too often researchers design questionnaires or

begin interviewing far too early ± before thinking through what information they require to

answer their research questions. Without attending to these research design matters at the

beginning, the conclusions drawn will normally be weak and unconvincing and fail to answer the

research question.

Design versus method

Research design is different from the method by which data are collected. Many research

methods texts confuse research designs with methods. It is not uncommon to see research design

treated as a mode of data collection rather than as a logical structure of the inquiry. But there is

nothing intrinsic about any research design that requires a particular method of data collection.

Although cross-sectional surveys are frequently equated with questionnaires and case studies are

often equated with participant observation (e.g. Whyte's Street Corner Society, 1943), data for

any design can be collected with any data collection method. How the data are collected is

irrelevant to the logic of the design. Failing to distinguish between design and method leads to

poor evaluation of designs. Equating cross-sectional designs with questionnaires, or case studies

with participant observation, means that the designs are often evaluated against the strengths and

weaknesses of the method rather than their ability to draw relatively unambiguous conclusions or

to select between rival plausible hypotheses.

Quantitative and qualitative research

Similarly, designs are often equated with qualitative and quantitative research methods. Social

surveys and experiments are frequently viewed as prime examples of quantitative research and

are evaluated against the strengths and weaknesses of statistical, quantitative research methods

and analysis. Case studies, on the other hand, are often seen as prime examples of qualitative

research ± which adopts an interpretive approach to data, studies `things' within their context and

considers the subjective meanings that people bring to their situation. It is erroneous to equate a

particular research design with either quantitative or qualitative methods. Yin (1993), a respected

authority on case study design, has stressed the irrelevance of the quantitative/ qualitative

distinction for case studies. He points out that:

‘’a point of confusion . . . has been the unfortunate linking between the case study method and

certain types of data collection ± for example those focusing on qualitative methods,

ethnography, or participant observation. People have thought that the case study method required

them to embrace these data collection methods . . . On the contrary, the method does not imply

any particular form of data collection ± which can be qualitative or quantitative’’ (1993: 32).

Similarly, Marsh (1982) argues that quantitative surveys can provide information and

explanations that are `adequate at the level of meaning'. While recognizing that survey research

has not always been good at tapping the subjective dimension of behaviour, she argues that:

‘’Making sense of social action . . . is . . . hard and surveys have not traditionally been very good

at it. The earliest survey researchers started a tradition . . . of bringing the meaning from outside,

either by making use of the researcher's stock of plausible explanations . . . or by bringing it from

subsidiary in-depth interviews sprinkling quotes . . . liberally on the raw correlations derived

from the survey. Survey research became much more exciting . . . when it began including

meaningful dimensions in the study design. [This has been done in] two ways, firstly [by] asking

the actor either for her reasons directly, or to supply information about the central values in her

life around which we may assume she is orienting her life. [This] involves collecting a

sufficiently complete picture of the context in which an actor finds herself that a team of

outsiders may read off the meaningful dimensions’’ (1982: 123-4).

Adopting a skeptical approach to explanations

The need for research design stems from a skeptical approach to research and a view that

scientific knowledge must always be provisional. The purpose of research design is to reduce the

ambiguity of much research evidence. We can always find some evidence consistent with almost

any theory. However, we should be skeptical of the evidence, and rather than seeking evidence

that is consistent with our theory we should seek evidence that provides a compelling test of the

theory. There are two related strategies for doing this: eliminating rival explanations of the

evidence and deliberately seeking evidence that could disprove the theory.

Plausible rival Hypotheses

A fundamental strategy of social research involves evaluating `plausible rival hypotheses'. We

need to examine and evaluate alternative ways of explaining a particular phenomenon. This

applies regardless of whether the data are quantitative or qualitative; regardless of the particular

research design (experimental, cross-sectional, longitudinal or case study); and regardless of the

method of data collection (e.g. observation, questionnaire). Our mindset needs to anticipate

alternative ways of interpreting findings and to regard any interpretation of these findings as

provisional ± subject to further testing.

3.2 Study Population

3.3 Sample Size

3.4 Sample Technique

Note: Present evidence about the sampling procedure used.

3.5 Sources of data

3.6 Research Instruments and Data Collection Procedure

3.7 Validity and Reliability

3.8 Data Analysis

Summary

This chapter has outlined the purpose of research design in both descriptive and explanatory

research. In explanatory research the purpose is to develop and evaluate causal theories. The

probabilistic nature of causation in social sciences, as opposed to deterministic causation, was

discussed.

Research design is not related to any particular method of collecting data or any particular type

of data. Any research design can, in principle, use any type of data collection method and can use

either quantitative or qualitative data. Research design refers to the structure of an enquiry: it is a

logical matter rather than a logistical one.

It has been argued that the central role of research design is to minimize the chance of drawing

incorrect causal inferences from data. Design is a logical task undertaken to ensure that the

evidence collected enables us to answer questions or to test theories as unambiguously as

possible. When designing research, it is essential that we identify the type of evidence required

to answer the research question in a convincing way. This means that we must not simply collect

evidence that is consistent with a particular theory or explanation. Research needs to be

structured in such a way that the evidence also bears on alternative rival explanations and

enables us to identify which of the competing explanations is most compelling empirically. It

also means that we must not simply look for evidence that supports our favourite theory: we

should also look for evidence that has the potential to disprove our preferred explanations.

NOTE: APA Style 6th Edition

OVERVIEW–The American Psychological Association (APA) style is widely accepted in the

social sciences and other fields, such as education, business, and nursing. The APA citation

format requires parenthetical citations within the text rather than endnotes or footnotes. Citations

in the text provide brief information, usually the name of the author and the date of publication,

to lead the reader to the source of information in the reference list at the end of the paper

References

1 Alhassan, S. (2011),” Market Access Capacity of Women Shea Processors in Ghana.

European Journal of Business Management”, 16-17

2 Boffa J. M., Yaméogo G., Nikiéma P., and Knudson D. M. (1996): Shea nut (Vitellaria

paradoxa) production and collection in agroforestry parklands of Burkina Faso. Department

of Forestry and Natural Resources, Purdue University, West Lafayette, Indiana, USA. FAO

document available at: http://www.fao.org/docrep/W3735e/w3735e17.htm

3 Carette C., Malotaux M., Van Leewen M., Tolkamp M. (2009): Shea nut and butter in

Ghana, opportunities and constraints for local processing. PDF document available at:

www.resilience-foundation.nl/docs/shea.

4 Carl G. Igo Martin J. Frick. Making Extension Efforts More Effective: A Case

Study of Malian Shea Butter Producers Assa Kante Oklahoma State University c/o Craig

Edwards 456 Agriculture Hall Stillwater, OK 74078 Phone: 405.744.8141 FAX:

405.744.5176 Email: assa_diarra_kante@hotmail.

5 Danida Forest Seed Centre (DFSC) (2000): Vitellaria paradoxa Gaertn. F. Seed Leaflet, no.

50.

6 Dogbevi E. K. (2009): Shea nut has economic and environmental values for Ghana. Sekaf

Ghana Ltd. Publication.

7 Elias M., Carney J. (2007): African Shea butter: a feminized subsidy from nature. Africa 77

(1): 37-62. Fao (1988a): Forest genetic resource priorities (Appendix 5). Report of Sixth

Session of the FAO Panel of Experts on Forest Gene Resources, 8-11 December, 1985,

Rome, Italy, 79, pp. 86-89.

8 Fobil J. N. (2007): Bole, Ghana: Research and development of the shea tree and its products,

New Haven. CT: HORIZON Solutions International, May 8, 2007. Available at:

http://www.solutionssite.org/ artman/publish [Accessed 2011-04-07].

9 F. Mohammed, S Boateng & S. Al-hassan (2013). Effects of Adoption of Improved Shea

butter Processing Technology on Women’s Livelihoods and their Microenterprise Growth.

10 Ghana Living Standards Survey (2015): Report of fifth round (GLSS 5). Ghana Statistical

Service. September 2008.

11 Kwode P. A. (2010): Feature article: Shea nut and poverty alleviation in Northern Ghana.

Ghana News Agency.

12 Lovett P. N., Haq N. (2000): Evidence for anthropic selection of the Sheanut tree (Vitellaria

paradoxa).

13 Manasieva E. (2011): The Mali women – empowerment through beauty. UNIDO project.

April, 2011.

14 Mohammed. F, Boateng S & Al-Hassan’s (2013): Effects of Adoption of Improved

Sheabutter Processing Technology on Women’s Livelihoods and their Microenterprise

Growth. American Journal of Humanities and Social Sciences Vo1. 1, No. 4, 2013, 244-250

DOI: 10.11634/232907811301419

15 Stichting N. V. 2006. Improving Market Access for Smallholder Farmers:

Concept note on developing Shea market chains. SNV Shea Subsector Study

Akuapem+North+2010PHC.pdf

AKWAPEM NORTH MUNICIPAL

ii

Copyright © 2014 Ghana Statistical Service

iii

PREFACE AND ACKNOWLEDGEMENT

No meaningful developmental activity can be undertaken without taking into account the

characteristics of the population for whom the activity is targeted. The size of the population

and its spatial distribution, growth and change over time, in addition to its socio-economic

characteristics are all important in development planning.

A population census is the most important source of data on the size, composition, growth

and distribution of a country’s population at the national and sub-national levels. Data from

the 2010 Population and Housing Census (PHC) will serve as reference for equitable

distribution of national resources and government services, including the allocation of

government funds among various regions, districts and other sub-national populations to

education, health and other social services.

The Ghana Statistical Service (GSS) is delighted to provide data users, especially the

Metropolitan, Municipal and District Assemblies, with district-level analytical reports based

on the 2010 PHC data to facilitate their planning and decision-making.

The District Analytical Report for the Akwapem North Municipal is one of the 216 district

census reports aimed at making data available to planners and decision makers at the district

level. In addition to presenting the district profile, the report discusses the social and

economic dimensions of demographic variables and their implications for policy formulation,

planning and interventions. The conclusions and recommendations drawn from the district

report are expected to serve as a basis for improving the quality of life of Ghanaians through

evidence-based decision-making, monitoring and evaluation of developmental goals and

intervention programmes.

For ease of accessibility to the census data, the district report and other census reports

produced by the GSS will be disseminated widely in both print and electronic formats. The

report will also be posted on the GSS website: www.statsghana.gov.gh.

The GSS wishes to express its profound gratitude to the Government of Ghana for providing

the required resources for the conduct of the 2010 PHC. While appreciating the contribution

of our Development Partners (DPs) towards the successful implementation of the Census, we

wish to specifically acknowledge the Department for Foreign Affairs, Trade and

Development (DFATD) formerly the Canadian International Development Agency (CIDA)

and the Danish International Development Agency (DANIDA) for providing resources for

the preparation of all the 216 district reports. Our gratitude also goes to the Metropolitan,

Municipal and District Assemblies, the Ministry of Local Government, Consultant Guides,

Consultant Editors, Project Steering Committee members and their respective institutions for

their invaluable support during the report writing exercise. Finally, we wish to thank all the

report writers, including the GSS staff who contributed to the preparation of the reports, for

their dedication and diligence in ensuring the timely and successful completion of the district

census reports.

Dr. Philomena Nyarko

Government Statistician

iv

TABLE OF CONTENTS

PREFACE AND ACKNOWLEDGEMENT ....................................................................... iii

LIST OF TABLES .................................................................................................................. vi

LIST OF FIGURES ............................................................................................................... vii

ACRONYMS AND ABBREVIATIONS ............................................................................ viii

EXECUTIVE SUMMARY .................................................................................................... ix

CHAPTER ONE: INTRODUCTION .................................................................................. 1

1.1 Introduction .................................................................................................................... 1

1.2 Physical Features ........................................................................................................... 1

1.3 Political and Administration Structures ......................................................................... 3

1.4 Cultural and Social Structure ......................................................................................... 3

1.5 Economy ........................................................................................................................ 4

1.6 Census Methodology, Concepts and Definitions ....................................................... 4

1.7 Organization of the report ............................................................................................ 14

CHAPTER TWO: DEMOGRAPHIC CHARACTERISTICS ........................................ 16

2.1 Introduction .................................................................................................................. 16

2.2 Population size and distribution ................................................................................... 16

2.3 Age structure ................................................................................................................ 17

2.4 Migration, Fertility and Mortality ................................................................................ 19

CHAPTER THREE: SOCIAL CHARACTERISTICS ................................................... 24

3.1 Introduction .................................................................................................................. 24

3.2 Household size, composition and structure ................................................................. 24

3.3 Marital Status ............................................................................................................... 26

3.4 Nationality.................................................................................................................... 29

3.5 Religious affiliations .................................................................................................... 30

3.6 Literacy and Education ................................................................................................ 31

CHAPTER FOUR: ECONOMIC CHARACTERISTICS ............................................... 35

4.1 Introduction .................................................................................................................. 35

4.2 Economic Activity Status ............................................................................................ 35

4.3 Occupation ................................................................................................................... 37

4.4 Industry ........................................................................................................................ 37

4.5 Employment Status ...................................................................................................... 38

4.6 Employment Sector ...................................................................................................... 38

CHAPTER FIVE: INFORMATION COMMUNICATION TECHNOLOGY .............. 40

5.1 Introduction .................................................................................................................. 40

5.2 Ownership of mobile phones ....................................................................................... 40

5.3 Use of Internet Facilities .............................................................................................. 40

5.4 Household Ownership of Desktop or Laptop Computers ............................................ 41

CHAPTER SIX: DISABILITY ........................................................................................... 42

6.1 Introduction .................................................................................................................. 42

6.2 Population with Disability ........................................................................................... 42

6.3 Disability and economic activity status ....................................................................... 43

v

6.4 Disability and Education .............................................................................................. 44

CHAPTER SEVEN: AGRICULTURAL ACTIVITIES .................................................. 46

7.1 Introduction .................................................................................................................. 46

7.2 Households in Agriculture ........................................................................................... 46

7.3 Distribution of livestock, animals reared and keepers ................................................. 47

CHAPTER EIGHT: HOUSING CONDITIONS .............................................................. 49

8.1 Introduction .................................................................................................................. 49

8.2 Housing stock............................................................................................................... 49

8.3 Type of dwelling units ................................................................................................. 49

8.4 Main material for outer walls ....................................................................................... 50

8.5 Main materials for floors ............................................................................................. 51

8.6 Main roofing materials ................................................................................................. 51

8.7 Room occupancy .......................................................................................................... 52

8.8 Access to utilities and household facilities .................................................................. 52

8.9 Bathing and toilet facilities .......................................................................................... 56

8.10 Method of Waste Disposal ....................................................................................... 57

CHAPTER NINE: SUMMARY OF FINDINGS, CONCLUSIONS AND POLICY

IMPLICATIONS ................................................................................ 59

9.1 Introduction .................................................................................................................. 59

9.2 Summary of findings.................................................................................................... 59

9.3 Conclusions and policy implications ........................................................................... 64

REFERENCES ....................................................................................................................... 66

APPENDICES ........................................................................................................................ 67

LIST OF CONTRIBUTORS ................................................................................................ 71

vi

LIST OF TABLES

Table 2.1: Population by age, sex and type of locality .......................................................... 16

Table 2.2: Population size by locality of residence and sex ratio ........................................... 17

Table 2.3: Population by broad age group and sex ................................................................. 17

Table 2.4: Age Dependency ratio by sex ................................................................................ 18

Table 2.5: Reported total fertility rate, general fertility rate and crude birth rateby district .. 19

Table 2.6: Female population 12 years and older by age, children ever born, children

surviving and sex of child ...................................................................................... 20

Table 2.7: Household deaths (within 12 months preceeding the census) by age, cause of

death and sex .......................................................................................................... 21

Table 2.8: Household deaths (within 12 months preceeding the census) by age, sex and

female pregnancy deaths ........................................................................................ 22

Table 2.9: Birthplace by duration of residence of migrants .................................................... 23

Table 3.1: Household size by sex of household head ............................................................ 24

Table 3.2: Household composition by sex .............................................................................. 25

Table 3.3: Household population by structure and sex ........................................................... 26

Table 3.4: Persons 12 years and older by sex, age-group and marital status .......................... 27

Table 3.5: Persons 12 years and older by sex, marital status and level of education ............ 28

Table 3.6: Persons 12 years and older by sex, marital status and economic activity status .. 29

Table 3.7: Population by nationality and sex .......................................................................... 30

Table 3.8: Population by religion and sex ............................................................................ 30

Table 3.9: Population 11 years and older by sex, age and literacy status ............................... 32

Table 3.10: Population 3 years and older by level of education, school attendance

and sex ................................................................................................................. 34

Table 4.1: Activity status of persons 15 years and older by sex ............................................. 35

Table 4.2: Population 15 years and older by sex, age and activity status ............................... 36

Table 4.3: Employed population 15 years and older by occupation and sex .......................... 37

Table 4.4: Employed population 15 years and older by employment status and sex ............. 38

Table 4.5: Employed population 15 years and older by employment sector and sex ........... 39

Table 5.1: Population 12 years and older by mobile phone ownership,

internet facility usage, and sex ............................................................................... 40

Table 5.2: Households having desktop/laptop computers by sex of head .............................. 41

Table 6.1: Population by type of locality, disability type and sex .......................................... 43

Table 6.2: Persons 15 years and older with disability by economic activity status and sex ... 44

Table 6.3: Population 3 years and older by sex, disability type and level of education ......... 45

Table 7.1: Size of households by agricultural activities ......................................................... 46

Table 7.2: Distribution of livestock, other animals and keepers............................................. 48

Table 8.1: Stock of houses and households ............................................................................ 49

Table 8.2: Type of dwelling by sex of household head and type of locality .......................... 50

Table 8.3: Main construction material for outer wall ............................................................. 50

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Table 8.4: Main construction materials for the floor .............................................................. 51

Table 8.5: Main construction material for roofing.................................................................. 51

Table 8.6: Household size and number of sleeping rooms occupied in dwelling unit ........... 52

Table 8.7: Main source of lighting .......................................................................................... 53

Table 8.8: Main source of cooking fuel, and cooking space used by households .................. 54

Table 8.9: Main source of water for drinking and other domestic purposes .......................... 55

Table 8.10: Type of toilet facility and bathing facility used by household by type of

locality................................................................................................................. 56

Table 8.11: Method of solid and liquid waste disposal by type of locality ............................ 58

Table 1A: Household composition by type of locality ........................................................... 67

Table 2A: Population 3 years and older by sex, disability type and level of education ......... 68

LIST OF FIGURES

Figure 1.1: District map of Akwapem North ............................................................................ 2

Figure 2.1: Population pyramid of Akwapem North .............................................................. 18

Figure 4.1: Employed population 15 years and older by employment sector ....................... 39

Figure 7.1: Percentage size of households in agriculture activities ........................................ 47

viii

ACRONYMS AND ABBREVIATIONS

CERGIS Centre for Geographical and Information Systems

CIDA Canadian International Development Agency

DANIDA Danish International Development Agency

DCD District Coordinating Director

DCE District Chief Executive

ECOWAS Economic Community of West African States

FCUBE Free Compulsory Universal Basic Education

GFR General Fertility Rate

GSS Ghana Statistical Service

ICT Information Communication Technology

IMIS Integrated Management Information System

JHS Junior High School

JSS Junior Secondary School

L.I Legislative Instrument

MMDAs Metropolitan, Municipal, District Assemblies

NGO Non-Governmental Organisation

PHC Population and Housing Census

PWDs Persons with Disabilities

SHS Senior High School

SSS Senior Secondary School

TFR Total Fertility Rate

UN United Nation

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EXECUTIVE SUMMARY

Introduction

The Municipal census report is the first of its kind since the first post-independence census

was conducted in 1960. The report provides basic information about the Municipality. It

gives a brief background of the Municipality, describing its physical features, political and

administrative structure, socio-cultural structure and economy. Using data from the 2010

Population and Housing Census (2010 PHC), the report discusses the population

characteristics of the Municipality, fertility, mortality, migration, marital status, literacy and

education, economic activity status, occupation, employment; Information Communication

Technology (ICT), disability, agricultural activities and housing conditions. The key findings

of the analysis are as follows (references are to the relevant sections of the report):

Population size, structure and composition

The population of Akwapem North Municipality, according to the 2010 Population and

Housing Census, is 136,483 representing 5.2 percent of the Eastern region’s total population

of 263,3154. Females constitute 53.1 percent and males represent 46.9 percent. Close to two

thirds (63.9%) of the population in the Municipality reside in rural localities. The

Municipality has a sex ratio of 88.4. The population of the Municipality is youthful (36.7%)

depicting a broad base population pyramid which tapers off with a small number of elderly

persons (12.9%). The total age dependency ratio (population less than 15 years and 65 years

and older to the population aged 15-64 years) for the Municipality is 81.3.

Fertility, mortality and migration

The Total Fertility Rate (TFR) for the Municipality 3.6 which slightly higher than the

Regional average of (3.55). The General Fertility Rate (GFR) is 103.4 births per 1000 women

aged 15-49 years which is slightly lower than the regional average of 103.9. The Crude Birth

Rate (CBR) is 26 per 1000 population. The crude death rate for the Municipality is 9.6 per

1000. The Municipality has a total migrant population of 45,183, representing 33.3 percent of

the total population. The majority (58.3%) of the migrant population were born in other

localities in the Eastern region. Of the migrants born in other regions in Ghana, the majority

(25.7%) were born in the Volta region.

Household size, composition and structure

The Municipality has a household population of 134,359 with a total number of 33,322

households. The average household size in the Municipality is 4.0 persons. Children of head of

household constitute the largest proportion (38.0%) of household members with head of

households forming about one quarter (24.8%) of household members. Single person

households constitute the highest (20.4%) of the households in the Municipality.

Marital status

Almost four out of every ten (38.8%) of the population aged 12 years and older are married

with a slightly higher percent (40.9%) indicating they have never been married. About 17

percent (16.3%) of the population aged 12 years and older had once been married but are

divorced, widowed or separated. Only four percent population aged 12 years and older are in

consensual unions. A higher percentage of females (5.4%) are separated than males 2.4%). A

x

similar trend is observed for the population widowed, where a higher percentage is recoded for

females (12.5) than females 2.4%).

Nationality and religious affiliation

Almost all the people in the Municipality are Ghanaians by birth (97.5%). Persons with dual

nationality constitute only 1.2 percent and Ghanaians by naturalization form less than one

percent (0.3%). Persons born outside Ghana constitute just about two percent (1.9%).

Christians constitute the majority (88.9%) of the population in the Municipality, followed by

Moslems (2.2%). Persons who reported as having no religious affiliation constitute 6.0

percent. The majority (33.5%) of Christians in the Municipality are Protestants (41.1%),

followed by Pentecostal/Charismatic (33.5%) and other Christians (11.2%). Catholics

recorded the lowest proportion of Christians in the Municipality (3.1%).

Literacy and education

More than three quarters (85%) of the population aged 11 years and older in the Akwapem

North Municipality are literate. Literacy is higher for the male (90.1%) population than

females (79.1%). The majority (70.2%) of the population can read and write in both English

and a Ghanaian language.

Current school attendance

The majority (87.4%) of the population currently in school are in the basic school level

(nursery, kindergarten, primary and JHS). Less than one tenth of the population currently in

school are in senior high schools, with only about three percent in tertiary. Less than one

percent (0.4%) are in vocational/technical/commercial schools.

Economic activity status and employment

Two thirds (66.1%) of the population aged 15 years and older in the Municipality are

economically active. Of the economically active population, about 92 percent (91.9%) are

employed. A higher proportion of males (67.7%) than females (64.7%) are economically

active. Among the economically active population, slightly more males (92.1%) than females

(91.7%) are employed. The majority (41.3%) of the economically not active population are

persons in full time education.

Occupation and industry of employment

In terms of occupation of the employed population, majority (37.0%) are engaged as skilled

agricultural, forestry and fishery workers. The second commonest (22.1%) occupation of the

employed is service and sales followed by craft and related work (17.8%). The major industry

engaging majority of the workers in the Municipality is also agriculture, forestry and fishing

(17.4%). This is followed by wholesale and retail trade (17.7%) and manufacturing (12.0%).

Employment status and sector

Majority (67.5%) of the employed population in the Municipality are self-employed without

employees with 18 percent being employees. Males (23.5%) are more likely to be employees

than females (11.8%). The reverse is the case for persons who are employed without

employees, where females have a higher percentage (73.6%) than males (60.2%). Majority

(86.1%) of the working population are in private informal sector. The next important sector is

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the public (government) sector which employed 9.0 percent of the workers. Only 4.5 percent

of the employed population work in the private formal sector.

Information Communication Technology

The use of mobile phones in the Municipality is quite extensive with 47.7 percent of the

population 12 years and older having mobile phones. There are relatively more females

(53.1%) than males (49.3%) having mobile phones. Less than six percent (5.2%) of

households in the Municipality own desktop/laptop computer(s). The use of internet is also

quite low, with only about six percent of the population aged 12 years and older using

internet facilities.

Disability

Three percent of the Municipality’s population have one form of disability or the other. The

proportion of the female population with disability is slightly higher (3.2%) than males

(2.7%). The types of disability in the Municipality include sight, hearing, speech, physical,

intellect, and emotional. Persons with sight disability recorded the highest percentage

(29.0%) followed closely by physical disability (27.0%). More than one third (38.5%) of the

population with disability have never been to school, 49 percent have ever attended school,

with only 9.7 indicating they have had secondary school education. The majority (59.4%) of

PWDs are not economically active.

Agriculture

Close half (47.1%) of the households in the Municipality are engage in agriculture. With

regard to locality of residence, there are more agricultural households in the rural areas

(58.6%) than urban areas (27.1%). The major agricultural activity in the municipality is crop

farming (93.5%), followed by livestock rearing (34.6%) and tree planting (1.7%). A higher

proportion (94.9%) of rural agricultural households are engaged in crop farming compared to

urban agricultural households (88.4%).

Housing stock

The housing stock of the Akwapim North Municipality is 22,896 representing 5.3 percent of

the total number of houses in the Eastern region. The average number of persons per house is

6. The majority of houses in the Municipality are located in rural localities (69.0%). The

average population per house in the Municipality is 5.9 persons which is almost equal to the

regional average of 6.0 persons.

Material for construction of outer wall, floor and roof

The most common construction material for outer walls of dwelling units in the Municipality

are cement block/concrete (63.6%) and mud/mud bricks/earth (28.7%). Similarly,

cement/concrete is the main (78.4%) material used in the construction of floors of dwelling

units in the Municipality. Metal sheets are the main roofing materials, constituting 91.2

percent of the roofs of dwelling units in the Municipality.

Room occupancy

One room constitutes the highest percentage (53.4%) of sleeping rooms occupied by

household in housing units in the Municipality. The second most occupied types of sleeping

rooms in occupied dwelling units is two sleeping rooms (26.6%) followed by three sleeping

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rooms (10.8%). About eight percent (7.9%) of households with 10 or more members occupy

single rooms.

Utilities and household facilities

Electricity is the main source of lighting for most households (60.5%) in the Municipality.

The percentage of households using electricity as their main source of lighting exceeds the

regional average of 58.5 percent. This is followed distantly by kerosene lamps (18.8%) with

flashlight/torch (8.5%) placing third. The percentage of urban households using electricity

(82.4%) far exceeds that for rural households (47.9%). More than one third (38.2%) of rural

households use kerosene lamps as their main source of lighting, against 13.2 percent for

urban households.

The majority (26.1%) of households in the Municipality use borehole water as their main source

of drinking water. The next most common source of drinking water is public standpipes (21.1%),

followed by pipes inside dwelling units (12.2%). A little over one tenth (10.2%) of households

depend on sachet water as their main source of drinking water. With regards to water for other

domestic use, the majority (27.4%) of households us borehole water, followed by river or stream

water (23.9%).

Firewood is the main source (40.8%) of energy for cooking for households in the Municipality is

firewood (40.8%) followed by charcoal (39.0%). The proportion of households using gas is also

quite high (16.1%).The majority of households use pit latrine 933.2%), followed by public toilet

(29.2%). About 5.0 percent of households reported that they had no toilet facilities and therefore

resorted to bush/beach/open fields.

Waste disposal

The most common (49.3%) method of solid waste disposal used by households is dumping

onto public dump (open space), followed by burning by households (21.5%). Routine collection

of waste from houses is hardly practiced (3.4%). Those who dump onto containers in public

dumps constitute 11.9 percent. About another 6.0 percent of households dumped their solid

wastes indiscriminately. For liquid waste disposal, the most common method used by

households is throwing the waste onto the compound (45.1%). A substantially low proportion

of households disposed of their liquid waste through a sewerage system (0.2%).

1

CHAPTER ONE

INTRODUCTION

1.1 Introduction

The Akwapem North Municipality was established in 1988 by Legislative Instrument (L.I.)

143. Until then, it was part of the Akwapem Municipal Council which was established in

1975 with Akropong as the district Capital.

It was elevated to Municipal Assembly status by LI 2124 in the exercise of power conferred

on the Minister responsible for the Local Government and Rural Development by subsection

(1) of Section 3 of the Local Government Act, 1993, (Act 462). This Instrument was made

on 15th day of March, 2012.

1.2 Physical Features

1.2.1 Location and size

The municipal area is located in the south-eastern part of the Eastern Region and is about 58

km from Accra, the capital city of Ghana. The Akwapem North Municipal shares boundaries

to the northeast with Yilo Krobo, north with New Jauben Municipal, southeast with Dangbe

West, southwest with Akwapem South District, and in the west with Suhum-Kraboa-Coaltar

District. The District covers a land area of about 450 sq. km representing 2.3percent of the

total area of the Eastern Region. The Akwapem North Municipal has about 230 settlements.

1.2.2 Climate and vegetation

The area experiences tropical rainfall pattern and wet semi-equatorial climate with maximum

rainfall occurring between May and August and minimum between September and

November. The mean rainfall is estimated to be in the range of 1250mm and 1270mm. Mean

temperatures fall between 20ᴼC and 24ᴼC with day time temperatures ranging between 25ᴼC

and 30ᴼC and night temperatures ranging between 13ᴼC and 24ᴼC Thus, temperatures are

mostly lower in the night. The dry season is from December to February when it is also dry

while the wet season is from March to October.

Vegetation is semi-broken forest with shrub and bush. There are two major forest reserves,

and a lot of forest patches and sacred groves scattered all over the Municipal. The forest

reserves are rich in various species such as ebony, odom, sapele and sanfram, among other

rear timber species.

1.2.3 Topography and drainage

The topography of the municipal is largely characterized by one main hill range called the

Akwapem Range with heights ranging between 381 metres and 488 metres and its highest

peak reaching 500 metres and situated at Amanokrom closer to a natural water tank. Its

lowest point is approximately, 152 metres. The rivers Brump, Ponpon and Aponapong and

their tributaries form the main drainage system in the Larteh-Mampong geographical area

while the Aboabo, Nsaki and Yensi streams drain the North-west areas of the municipal’s

landscape.

2

Figure 1.1: District map of Akwapem North

Source: Ghana Statistical Service, GIS

3

1.3 Political and Administration Structures

The Akwapem North Municipal Assembly has a decentralized political and administrative

unit like other districts in Ghana. Under the local Government Act 1993, the Assembly is

responsible for the overall development of the district, including planning, budgeting and

implementation of development policies and programmes.

The Assembly is composed of the following:

i. Municipal Chief Executive

ii. Elected Assembly Members

iii. Other Members appointed by His Excellency the President of the Republic of Ghana.

iv. Members of Parliament.

The political head is the Municipal Chief Executive who is responsible for the

implementation of Central Government Programmes. Administratively, a Municipal

Coordinating Director sees to the day-to-day administration of the municipal. The Assembly

is subdivided into eleven (11) areas and four (4) town councils.

1.4 Cultural and Social Structure

1.4.1 Ethnicity and Language

On the whole, three major languages are spoken in the Municipality- Twi (Akwapim Twi),

Kyerepong and Guan. Akwapem-Twi speaking people are the largest ethnic group in the

Municipal, representing 51.6% of the population, 42.3% are of Kyerepong and Guan, while

only 6.1% constitutes Ewes, persons speaking languages of northern ethnic groups, Krobo

and other ethnic groups. With Akwapem Twi spoken by almost all the residents in the

Municipal and the most widespread medium of mass communication and functional

education as well as development information dissemination in the district.

1.4.2 Traditional structure

There are five principal divisional chiefs in the Akwapem North Traditional Area. They have

two sub-divisional chiefs with the paramount chief of the Akwapem Traditional Area being

the Omanhene. Omanhene is the custodian of Akwapem North stool lands, ensures

performance of rites (festivals and other rites to gods) and owes allegiance to his people to

rule in peace and fairness. The people of Akwapem North celebrate the Odwira Festival.

1.4.3 Religious affiliation

All the major types of religious groups in Ghana are found in the district: Traditional

worshippers, Christians and Moslem. However, the predominant religion in the municipal is

Christianity, constituting 86.8%. This is followed by Islam (10.2%) and the remaining 3%

belong to the other religious groups, including Traditional worshippers.

1.4.4 Tourist attraction

The Municipal is endowed with many interesting tourist attraction sites which need to be

developed to attract potential tourists as well as boost the Internally Generated Fund of the

district assembly. These include the waterfalls at Asenema, Akyeremanteng (Akaa falls),

4

Nsuta, Dawu, Obosomase, Asuoyaa and Amenapa. Plans to attract potential investors to

assist in developing the area have been initiated by the assembly.

There is also the legendary Okomfo Anokye at Awukugua, famous Akonede Shrine at

Larteh, the Slave cave and ancient slave route at Obom, the Obosabea and legendary

Fontonfrom drum at Akyeremanteng.

1.5 Economy

Agriculture and related trades is the major source of occupation for the majority of the people

in the municipality but level of production is mostly subsistence. The food crops cultivated

include cassava, plantain, cocoyam, maize and vegetables. Gari processing is the most

evident agro-processing enterprise in the municipal and the Assembly is constantly making

efforts to promote private investment in large-scale cassava cultivation. Cassava could be

produced on a large scale from which starch will be extracted and other end products used as

raw materials in brewery industries in Ghana.

The most flourishing industries are the gari processing small-scale industry, secretarial

services, restaurants, food vending on tabletop and production of pure water. There are also

sellers of iron wares, electrical gadgets and appliances and transport owners.

1.6 Census Methodology, Concepts and Definitions

1.6.1 Introduction

Ghana Statistical Service (GSS) was guided by the principle of international comparability

and the need to obtain accurate information in the 2010 Population and Housing Census

(2010 PHC). The Census was, therefore, conducted using all the essential features of a

modern census as contained in the United Nations Principles and Recommendations for

countries taking part in the 2010 Round of Population and Housing Censuses.

Experience from previous post independence censuses of Ghana (1960, 1970, 1984 and 2000)

was taken into consideration in developing the methodologies for conducting the 2010 PHC.

The primary objective of the 2010 PHC was to provide information on the number,

distribution and social, economic and demographic characteristics of the population of Ghana

necessary to facilitate the socio-economic development of the country.

1.6.2 Pre-enumeration activities

Development of census project document and work plans

A large scale statistical operation, such as the 2010 Population and Housing Census required

meticulous planning for its successful implementation. A working group of the Ghana

Statistical Service prepared the census project document with the assistance of two

consultants. The document contains the rationale and objectives of the census, census

organisation, a work plan as well as a budget. The project document was launched in

November 2008 as part of the Ghana Statistics Development Plan (GSDP) and reviewed in

November 2009.

5

Census secretariat and committees

A well-structured management and supervisory framework that outlines the responsibilities

of the various stakeholders is essential for the effective implementation of a population and

housing census. To implement the 2010 PHC, a National Census Secretariat was set up in

January 2008 and comprised professional and technical staff of GSS as well as staff of other

Ministries, Departments and Agencies (MDAs) seconded to GSS. The Census Secretariat was

primarily responsible for the day-to-day planning and implementation of the census activities.

The Secretariat had seven units, namely; census administration, cartography, recruitment and

training, publicity and education, field operations and logistics management, data processing,

and data analysis and dissemination.

The Census Secretariat was initially headed by an acting Census Coordinator engaged by the

United Nations Population Fund (UNFPA) in 2008 to support GSS in the planning of the

Census. In 2009, the Census Secretariat was re-organised with the Government Statistician as

the National Chief Census Officer and overall Coordinator, assisted by a Census

Management Team and a Census Coordinating Team. The Census Management Team had

oversight responsibility for the implementation of the Census. It also had the responsibility of

taking critical decisions on the census in consultation with other national committees. The

Census Coordinating Team, on the other hand, was responsible for the day-to-day

implementation of the Census programme.

A number of census committees were also set up at both national and sub-national levels to

provide guidance and assistance with respect to resource mobilization and technical advice.

At the national level, the committees were the National Census Steering Committee (NCSC),

the National Census Technical Advisory Committee (NCTAC) and the National Census

Publicity and Education Committee (NCPEC). At the regional and District levels, the

committees were the Regional Census Implementation Committee and the District Census

Implementation Committee, respectively.

The Regional and District Census Implementation Committees were inter-sectoral in their

composition. Members of the Committees were mainly from decentralized departments with

the Regional and District Coordinating Directors chairing the Regional Census

Implementation Committee and District Census Implementation Committee, respectively.

The Committees contributed to the planning of District, community and locality level

activities in areas of publicity and field operations. They supported the Regional and District

Census Officers in the recruitment and training of field personnel (enumerators and

supervisors), as well as mobilizing logistical support for the census.

Selection of Census topics

The topics selected for the 2010 Population and Housing Census were based on

recommendations contained in the UN Principles and Recommendations for 2010 Round of

Population and Housing Censuses and the African Addendum to that document as well as the

needs of data users. All the core topics recommended at the global level, i.e., geographical

and internal migration characteristics, international migration, household characteristics,

demographic and social characteristics such as age, date of birth, sex, and marital status,

fertility and mortality, educational and economic characteristics, issues relating to disability

and housing conditions and amenities were included in the census.

6

Some topics that were not considered core by the UN recommendations but which were

found to be of great interest and importance to Ghana and were, therefore, included in the

2010 PHC are religion, ethnicity, employment sector and place of work, agricultural activity,

as well as housing topics, such as, type of dwelling, materials for outer wall, floor and roof,

tenure/holding arrangement, number of sleeping rooms, cooking fuel, cooking space and

Information Communication Technology (ICT).

Census mapping

A timely and well implemented census mapping is pivotal to the success of any population

and housing census. Mapping delineates the country into enumeration areas to facilitate

smooth enumeration of the population. The updating of the 2000 Census Enumeration Area

(EA) maps started in the last quarter of 2007 with the acquisition of topographic sheets of all

indices from the Survey and Mapping Division of the Lands Commission. In addition, digital

sheets were also procured for the Geographical Information System Unit.

The Cartography Unit of the Census Secretariat collaborated with the Survey and Mapping

Division of the Lands Commission and the Centre for Remote Sensing and Geographic

Information Services (CERSGIS) of the Department of Geography and Resource

Development, University of Ghana, to determine the viability of migrating from analog to

digital mapping for the 2010 PHC, as recommended in the 2000 PHC Administrative Report.

Field cartographic work started in March 2008 and was completed in February 2010.

Development of questionnaire and manuals

For effective data collection, there is the need to design appropriate documents to solicit the

required information from respondents. GSS consulted widely with main data users in the

process of the questionnaire development. Data users including MDAs, research institutions,

civil society organisations and development partners were given the opportunity to indicate

the type of questions they wanted to be included in the census questionnaire.

Documents developed for the census included the questionnaire and manuals, and field

operation documents. The field operation documents included Enumerator’s Visitation

Record Book, Supervisor’s Record Book, and other operational control forms. These record

books served as operational and quality control tools to assist enumerators and supervisors to

control and monitor their field duties respectively.

Pre-tests and trial census

It is internationally recognized that an essential element in census planning is the pre-testing

of the questionnaire and related instructions. The objective of the pre-test is to test the

questionnaire, the definition of its concepts and the instructions for filling out the

questionnaire.

The census questionnaire was pre-tested twice in the course of its development. The first pre-

test was carried out in March 2009 to find out the suitability of the questions and the

instructions provided. It also tested the adequacy and completeness of the responses and how

respondents understood the questions. The second pre-test was done in 10 selected

enumeration areas in August, 2009. The objective of the second pre-test was to examine the

sequence of the questions, test the new questions, such as, date of birth and migration, and

assess how the introduction of ‘date of birth’ could help to reduce ‘age heaping’. With regard

to questions on fertility, the pre-tests sought to find out the difference, if any, between proxy

7

responses and responses by the respondents themselves. Both pre-tests were carried in the

Greater Accra Region. Experience from the pre-tests was used to improve the final census

questionnaire.

A trial census which is a dress rehearsal of all the activities and procedures that are planned

for the main census was carried out in October/November 2009. These included recruitment

and training, distribution of census materials, administration of the questionnaire and other

census forms, enumeration of the various categories of the population (household,

institutional and floating population), and data processing. The trial census was held in six

selected Districts across the country namely; Saboba (Northern Region), Chereponi (Northern

Region), Sene (Brong Ahafo Region), Bia (Western Region), Awutu Senya (Central

Region), and Osu Klottey Sub-Metro (Greater Accra Region). A number of factors were

considered in selecting the trial census Districts. These included: administrative boundary

issues, ecological zone, and accessibility, enumeration of floating population/outdoor-

sleepers, fast growing areas, institutional population, and enumeration areas with scattered

settlements.

The trial census provided GSS with an opportunity to assess its plans and procedures as well

as the state of preparedness for the conduct of the 2010 PHC. The common errors found

during editing of the completed questionnaires resulted in modifications to the census

questionnaire, enumerator manuals and other documents. The results of the trial census

assisted GSS to arrive at technically sound decisions on the ideal number of persons per

questionnaire, number of persons in the household roster, migration questions, placement of

the mortality question, serial numbering of houses/housing structures and method of

collection of information on community facilities. Lessons learnt from the trial census also

guided the planning of the recruitment process, the procedures for training of census field

staff and the publicity and education interventions.

1.6.3 Census Enumeration

Method of enumeration and field work

All post- independence censuses (1960, 1970, 1984, and 2000) conducted in Ghana used the

de facto method of enumeration where people are enumerated at where they were on census

night and not where they usually reside. The same method was adopted for the 2010 PHC.

The de facto count is preferred because it provides a simple and straight forward way of

counting the population since it is based on a physical fact of presence and can hardly be

misinterpreted. It is thought that the method also minimizes the risks of under-enumeration

and over enumeration. The canvasser method, which involves trained field personnel visiting

houses and households identified in their respective enumeration areas, was adopted for the

2010 PHC.

The main census enumeration involved the canvassing of all categories of the population by

trained enumerators, using questionnaires prepared and tested during the pre-enumeration

phase. Specific arrangements were made for the coverage of special population groups, such

as the homeless and the floating population. The fieldwork began on 21st September 2010

with the identification of EA boundaries, listing of structures, enumeration of institutional

population and floating population.

The week preceding the Census Night was used by field personnel to list houses and other

structures in their enumeration areas. Enumerators were also mobilized to enumerate

8

residents/inmates of institutions, such as, schools and prisons. They returned to the

institutions during the enumeration period to reconcile the information they obtained from

individuals and also to cross out names of those who were absent from the institutions on

Census Night.

Out-door sleepers (floating population) were also enumerated on the Census Night.

Enumeration of the household population started on Monday, 27th September, 2010.

Enumerators visited houses, compounds and structures in their enumeration areas and started

enumerating all households including visitors who spent the Census Night in the households.

Enumeration was carried out in the order in which houses/structures were listed and where

the members of the household were absent, the enumerator left a call-back-card indicating

when he/she would come back to enumerate the household. The enumeration process took off

smoothly with enumerators poised on completing their assignments on schedule since many

of them were teachers and had to return to school. However, many enumerators ran short of

questionnaires after a few days’ work.

Enumeration resumed in all Districts when the questionnaire shortage was resolved and by

17th October, 2010, enumeration was completed in most Districts. Enumerators who had

finished their work were mobilized to assist in the enumeration of localities that were yet to

be enumerated in some regional capitals and other fast growing areas. Flooded areas and

other inaccessible localities were also enumerated after the end of the official enumeration

period. Because some enumeration areas in fast growing cities and towns, such as, Accra

Metropolitan Area, Kumasi, Kasoa and Techiman were not properly demarcated and some

were characterized by large EAs, some enumerators were unable to complete their assigned

tasks within the stipulated time.

1.6.4 Post Enumeration Survey

In line with United Nations recommendations, GSS conducted a Post Enumeration Survey

(PES) in April, 2011 to check content and coverage error. The PES was also to serve as an

important tool in providing feedback regarding operational matters such as concepts and

procedures in order to help improve future census operations. The PES field work was

carried out for 21 days in April 2011 and was closely monitored and supervised to ensure

quality output. The main findings of the PES were that:

 97.0 percent of all household residents who were in the country on Census Night (26th September, 2010) were enumerated.

 1.3 percent of the population was erroneously included in the census.

 Regional differentials are observed. Upper East region recorded the highest coverage rate of 98.2 percent while the Volta region had the lowest coverage rate of 95.7

percent.

 Males (3.3%) were more likely than females (2.8%) to be omitted in the census. The coverage rate for males was 96.7 percent and the coverage rate for females was 97.2

percent. Also, the coverage rates (94.1%) for those within the 20-29 and 30-39 age

groups are relatively lower compared to the coverage rates of the other age groups.

9

 There was a high rate of agreement between the 2010 PHC data and the PES data for sex (98.8%), marital status (94.6%), relationship to head of household (90.5%) and

age (83.0%).

1.6.5 Release and dissemination of results

The provisional results of the census were released in February 2011 and the final results in

May 2012. A National Analytical report, six thematic reports, a Census Atlas, 10 Regional

Reports and a report on Demographic, Social, Economic and Housing were prepared and

disseminated in 2013.

1.6.6 Concepts and Definitions

Introduction

The 2010 Population and Housing Census of Ghana followed the essential concepts and

definitions of a modern Population and Housing Census as recommended by the United

Nations (UN). It is important that the concepts, definitions and recommendations are adhered

to since they form the basis upon which Ghana could compare her data with that of other

countries.

The concepts and definitions in this report cover all sections of the 2010 Population and

Housing Census questionnaires (PHC1A and PHC1B). The sections were: geographical

location of the population, Household and Non-household population, Literacy and

Education, Emigration, Demographic and Economic Characteristics, Disability, Information

Communication Technology (ICT), Fertility, Mortality, Agricultural Activity and Housing

Conditions.

The concepts and definitions are provided to facilitate understanding and use of the data

presented in this report. Users are therefore advised to use the results of the census within the

context of these concepts and definitions.

Region

There were ten (10) administrative regions in Ghana during the 2010 Population and Housing

Census as they were in 1984 and 2000.

District

In 1988, Ghana changed from the local authority system of administration to the District

assembly system. In that year, the then existing 140 local authorities were demarcated into

110 Districts. In 2004, 28 new Districts were created; this increased the number of Districts

in the country to 138. In 2008, 32 additional Districts were created bringing the total number

of Districts to 170. The 2010 Population and Housing Census was conducted in these 170

administrative Districts (these are made-up of 164 Districts/municipals and 6 metropolitan

areas). In 2012, 46 new Districts were created to bring the total number of Districts to 216.

There was urgent need for data for the 46 newly created Districts for planning and decision-

making. To meet this demand, the 2010 Census data was re-programmed into 216 Districts

after carrying out additional fieldwork and consultations with stakeholders in the Districts

affected by the creation of the new Districts.

10

Locality

A locality was defined as a distinct population cluster (also designated as inhabited place,

populated centre, settlement) which has a NAME or LOCALLY RECOGNISED STATUS. It

included fishing hamlets, mining camps, ranches, farms, market towns, villages, towns, cities

and many other types of population clusters, which meet the above criteria. There were two

main types of localities, rural and urban. As in previous censuses, the classification of

localities into ‘urban’ and ‘rural’ was based on population size. Localities with 5,000 or more

persons were classified as urban while localities with less than 5,000 persons were classified

as rural.

Population

The 2010 Census was a “de facto” count and each person present in Ghana, irrespective of

nationality, was enumerated at the place where he/she spent the midnight of 26th September

2010.

Household

A household was defined as a person or a group of persons, who lived together in the same

house or compound and shared the same house-keeping arrangements. In general, a

household consisted of a man, his wife, children and some other relatives or a house help who

may be living with them. However, it is important to remember that members of a household

are not necessarily related (by blood or marriage) because non-relatives (e.g. house helps)

may form part of a household.

Head of Household

The household head was defined as a male or female member of the household recognised as

such by the other household members. The head of household is generally the person who has

economic and social responsibility for the household. All relationships are defined with

reference to the head.

Household and Non-household population

Household population comprised of all persons who spent the census night in a household

setting. All persons who did not spend the census night in a household setting (except

otherwise stated) were classified as non-household population. Persons who spent census

night in any of the under listed institutions and locations were classified as non-household

population:

(a) Educational Institutions

(b) Children's and Old People’s Homes

(c) Hospitals and Healing Centres

(d) Hotels

(e) Prisons

(f) Service Barracks

(g) Soldiers on field exercise

11

(h) Floating Population: The following are examples of persons in this category:

i. All persons who slept in lorry parks, markets, in front of stores and offices, public bathrooms, petrol filling stations, railway stations, verandas, pavements, and all such

places which are not houses or compounds.

ii. Hunting and fishing camps.

iii. Beggars and vagrants (mentally sick or otherwise).

Age

The age of every person was recorded in completed years disregarding fractions of days and

months. For those persons who did not know their birthdays, the enumerator estimated their

ages using a list of District, regional and national historical events.

Nationality

Nationality is defined as the country to which a person belongs. A distinction is made

between Ghanaians and other nationals. Ghanaian nationals are grouped into Ghanaian by

birth, Ghanaian with dual nationality and Ghanaian by naturalization. Other nationals are

grouped into ECOWAS nationals, Africans other than ECOWAS nationals, and non-

Africans.

Ethnicity

Ethnicity refers to the ethnic group that a person belonged to. This information is collected

only from Ghanaians by birth and Ghanaians with dual nationality. The classification of

ethnic groups in Ghana is that officially provided by the Bureau of Ghana Languages and

which has been in use since the 1960 census.

Birthplace

The birthplace of a person refers to the locality of usual residence of the mother at the time of

birth. If after delivery a mother stayed outside her locality of usual residence for six months

or more or had the intention of staying in the new place for six or more months, then the

actual town/village of physical birth becomes the birthplace of the child.

Duration of Residence

Duration of residence refers to the number of years a person has lived in a particular place.

This question is only asked of persons not born in the place where enumeration took place.

Breaks in duration of residence lasting less than 12 months are disregarded. The duration of

residence of persons who made multiple movements of one (1) year or more is assumed to be

the number of years lived in the locality (town or village) since the last movement.

Religion

Religion refers to the individual’s religious affiliation as reported by the respondent,

irrespective of the religion of the household head or the head’s spouse or the name of the

person. No attempt was made to find out if respondents actually practiced the faith they

professed.

12

Marital Status

Marital status refers to the respondent’s marital status as at Census Night. The question on

marital status was asked only of persons 12 years and older. The selection of the age limit of

12 years was based on the average age at menarche and also on the practice in some parts of

the country where girls as young as 12 years old could be given in marriage.

Literacy

The question on literacy referred to the respondent's ability to read and write in any language.

A person was considered literate if he/she could read and write a simple statement with

understanding. The question on literacy was asked only of persons 11 years and older.

Education

School Attendance

Data was collected on school attendance for all persons three (3) years and older. School

attendance refers to whether a person has ever attended, was currently attending or has never

attended school. In the census, school meant an educational institution where a person

received at least four hours of formal education.

Although the lower age limit of formal education is six years for primary one, eligibility for

the school attendance question was lowered to three years because pre-school education has

become an important phenomenon in the country.

Level of Education

Level of education refers to the highest level of formal school that a person ever attended or

was attending. This information was obtained for persons 3 years and older.

Activity Status

Activity status refers to economic or non-economic activity of respondents during the 7 days

preceding census night. Information on type of activity was collected on persons 5 years and

older. A person was regarded as economically active if he/she:

a. Worked for pay or profit or family gain for at least 1 hour within the 7 days preceding Census Night. This included persons who were in paid employment or self- employment

or contributing family workers.

b. Did not work, but had jobs to return to.

c. Were unemployed.

The economically not active were persons who did not work and were not seeking for work.

They were classified by reasons for not being economically active. Economically not active

persons included homemakers, students, retired persons, the disabled and persons who were

unable to work due to their age or ill-health.

Occupation

This referred to the type of work the person was engaged in at the establishment where he/she

worked. This was asked only of persons 5 years and older who worked 7 days before the

13

census night, and those who did not work but had a job to return to as well as those

unemployed who had worked before. All persons who worked during the 7 days before the

census night were classified by the kind of work they were engaged in. The emphasis was on

the work the person did during the reference period and not what he/she was trained to do.

For those who did not work but had a job to return to, their occupation was the job they

would go back to after the period of absence. Also, for persons who had worked before and

were seeking for work and available for work, their occupation was on the last work they did

before becoming unemployed. If a person was engaged in more than one occupation, only the

main one was considered.

Industry

Industry referred to the type of product produced or service rendered at the respondent’s work

place. Information was collected only on the main product produced or service rendered in

the establishment during the reference period.

Employment Status

Employment status refers to the status of a person in the establishment where he/she currently

works or previously worked. Eight employment status categories were provided: employee,

self-employed without employees, self-employed with employees, casual worker,

contributing family worker, apprentice, domestic employee (house help). Persons who could

not be classified under any of the above categories were classified as “other”.

Employment Sector

This refers to the sector in which a person worked. The employment sectors covered in the

census were public, private formal, private informal, semi-public/parastatal, NGOs and

international organizations.

Disability

Persons with disability were defined as those who were unable to or were restricted in the

performance of specific tasks/activities due to loss of function of some part of the body as a

result of impairment or malformation. Information was collected on persons with visual/sight

impairment, hearing impairment, mental retardation, emotional or behavioural disorders and

other physical challenges.

Information Communication Technology (ICT)

ICT questions were asked for both individuals and households. Persons having mobile

phones refer to respondents 12 years and older who owned mobile phones (irrespective of the

number of mobile phones owned by each person). Persons using internet facility refers to

those who had access to internet facility at home, internet cafe, on mobile phone or other

mobile device. Internet access is assumed to be not only via computer, but also by mobile

phones, PDA, game machine and digital television.

Households having Personal Computers/Laptops refer to households who own

desktops/laptop computers. The fixed telephone line refers to a telephone line connecting a

customer’s terminal equipment (e.g. telephone set, facsimile machine) to the public switch

telephone network.

14

Fertility

Two types of fertility data were collected: lifetime fertility and current fertility. Lifetime

fertility refers to the total number of live births that females 12 years and older had ever had

during their life time. Current fertility refers to the number of live births that females 12-54

years old had in the 12 months preceding the Census Night.

Mortality

Mortality refers to all deaths that occurred in the household during the 12 months preceding

the Census Night. The report presents information on deaths due to accidents, violence,

homicide and suicide. In addition, data were collected on pregnancy-related deaths of

females 12-54 years.

Agriculture

The census sought information on household members who are engaged in agricultural

activities, including the cultivation of crops or tree planting, rearing of livestock or breeding

of fish for sale or family consumption. Information was also collected on their farms, types of

crops and number and type of livestock.

Housing Conditions and Facilities

The UN recommended definition of a house as “a structurally separate and independent place

of abode such that a person or group of persons can isolate themselves from the hazards of

climate such as storms and the sun’’ was adopted. The definition, therefore, covered any type

of shelter used as living quarters, such as separate houses, semi-detached houses,

flats/apartments, compound houses, huts, tents, kiosks and containers.

Living quarters or dwelling units refer to a specific area or space occupied by a particular

household and therefore need not necessarily be the same as the house of which the dwelling

unit may be a part.

Information collected on housing conditions included the type of dwelling unit, main

construction materials for walls, floor and roof, holding/tenure arrangement, ownership type,

type of lighting, source of water supply and toilet facilities. Data was also collected on

method of disposal of solid and liquid waste.

1.7 Organization of the report

The report consists of nine chapters. Chapter one provides basic information about the

district. It gives a brief background of the district, describing its physical features, political

and administrative structure, social and cultural structure, economy and the methodology and

concepts used in the report. Chapter two discusses the population size, composition and age

structure. It further discusses the migratory pattern in the district as well as fertility and

mortality.

In chapter three, the focus is on household size, composition and headship as well as the

marital characteristics and nationality of the inhabitants of the district. The chapter also

discusses the religious affiliations and the educational statuses of the members of the district.

Chapter four focuses on economic characteristics such as economic activity status,

occupation, industries and the employment status and sectors that the people are employed.

15

Information Communication Technology (ICT) is discussed in chapter five. It analyses

mobile phone ownership, internet use and ownership of desktop/laptop computers while

chapter six is devoted to Persons living with disabilities (PWDs) and their socio-demographic

characteristics. Chapter seven concentrates on the agricultural activities of the households,

describing the types of farming activities, livestock rearing and numbers of livestock reared.

In chapter eight, housing conditions such as housing stock, type of dwelling and construction

materials, room occupancy, holding and tenancy, lighting and cooking facilities, bathing and

toilet facilities, waste disposal and source of water for drinking or for other domestic use in

the district are discussed and analysed in detail. The final chapter, Chapter nine presents the

summary of findings and conclusions. It also discusses the policy implications of the findings

for the district.

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CHAPTER TWO

DEMOGRAPHIC CHARACTERISTICS

2.1 Introduction

In order for policy and decision makers to take pragmatic steps to advance economic

development, there should be thorough analysis of the size, composition and distribution of a

population’s age and sex structure as well as migration, fertility and mortality. The objective

of this chapter is to analyse the 2010 Census data relating to population size and distribution

(urban/rural), age-sex structure (including dependency ratio), migration, fertility and

mortality in the Akwapem North Municipality.

2.2 Population size and distribution

Table 2.1 presents the population size as well the age and sex distribution of the population of

the Akwapem North Municipality. Also presented in the table are locality of residence of the

population and sex ratio. These characteristics are examined in the sub-sections that follow.

In some cases, simpler tables are derived from Table 2.1 to enhance the analysis of the

characteristics.

Table 2.1: Population by age, sex and type of locality

Age Group

Sex Sex

ratio

Type of locality

Both Sexes Male Female

Urban Rural

All Ages 136,483 64,028 72,455 88.4 46,562 89,921 0 - 4 17,425 8,924 8,501

105

5,045 12,380

5 - 9 16,291 8,374 7,917

105.8

5,005 11,286

12 - 14 16,313 8,170 8,143

100.3

5,419 10,894

15 - 19 14,184 7,324 6,860

106.8

4,985 9,199

20 - 24 11,228 5,282 5,946

88.8

3,896 7,332

25 - 29 9,928 4,315 5,613

76.9

3,376 6,552

30 - 34 8,194 3,835 4,359

88

2,842 5,352

35 - 39 7,295 3,321 3,974

83.6

2,479 4,816

40 - 44 6,190 2,768 3,422

80.9

2,129 4,061

45 - 49 5,468 2,397 3,071

78.1

1,973 3,495

50 - 54 5,389 2,153 3,236

66.5

1,957 3,432

55 - 59 3,672 1,581 2,091

75.6

1,367 2,305

60 - 64 3,741 1,581 2,160

73.2

1,441 2,300

65 - 69 2,535 1,015 1,520

66.8

1,001 1,534

70 - 74 3,147 1,179 1,968

59.9

1,210 1,937

75 - 79 2,068 785 1,283

61.2

908 1,160

80 - 84 1,665 535 1,130

47.3

716 949

85 - 89 925 278 647

43

454 471

90 - 94 562 142 420

33.8

256 306

95 - 99 263 69 194

35.6

103 160

All Ages 136,483 64,028 72,455

88.4

46,562 89,921

0-14 50,029 25,468 24,561

103.7

15,469 34,560

15-64 75,289 34,557 40,732

84.8

26,445 48,844

65+ 11,165 4,003 7,162

55.9

4,648 6,517

Age-dependency ratio 81.28 85.28 77.88 76.07 84.1 Source: Ghana Statistical Service, 2010 Population and Housing Census

From the table, the Municipality has a total population of 136,483, representing 5.2 percent of

Eastern region population. More than half (53.1%) of the population are females, resulting in

17

a sex ratio (number males to 100 females) of 88.8. Distribution of the population by locality

of residence indicates that the majority (63.9%) of the population in the Municipality live in

rural areas. The data further indicates as in Table 2.2 that, the sex ratio is higher in the rural

areas (92.3) compared to the urban areas (81.3).

Table 2.2: Population size by locality of residence and sex ratio

All Localities

Urban

Rural

Sex Number Percent Number Percent Number Percent

Total 136,483 100.0

46,562 100.0

89,921 100.0

Male 64,028 46.9

20,877 44.8

43,151 48.0

Female 72,455 53.1

25,685 55.2

46,770 52.0

Sex Ratio 88.4

81.3

92.3

Percent of Regional Population 5.2 4.1 6.0 Source: Ghana Statistical Service, 2010 Population and Housing Census

2.3 Age structure

Table 2.3 shows the distribution of the population by broad age groups and sex. As shown in

the table, more than one third (36.7%) of the Municipality’s population is under age 15 years.

The elderly population (persons aged 60 years and older) form a little over one tenth (10.9) of

the population. Overall, majority (63.6%) of the population are under age 30 years. Similar

age distribution is observed for the sexes, with the exception that there are more females than

males in the elderly population (12.9% vs. 8.7%), indicating higher mortality in the elderly

male population.

Table 2.3: Population by broad age group and sex

Age group

Both Sexes Males Females

Number Percent Number Percent Number Percent

All ages 136,483 100.0 64,028 100.0 72,455 100.0

0 - 14 50,029 36.7

25,468 39.8

24,561 33.9

15 - 24 25,412 18.6

12,606 19.7

12,806 17.7

25 - 29 9,928 7.3

4,315 6.7

5,613 7.7

30 - 59 36,208 26.5

16,055 25.1

20,153 27.8

60 + 14,906 10.9 5,584 8.7 9,322 12.9

Source: Ghana Statistical Service, 2010 Population and Housing Census

Population pyramid is a useful way of representing the age-sex structure of a population.

Figure 2.1 depicts the population pyramid of the Municipality. As shown in the figure, the

pyramid has a broad base, indicating concentration of the population in younger ages and a

conical top representing a small percentage of elderly persons. The figure further indicates

that a large new cohort is born every year as displayed at the bottom of the pyramid (ages 0-4

years). As cohorts age, they inevitably lose members either through death or migration or

both. With increasing age, the structure looks slightly thinner for the males than for the

females, indicating that, at the older ages, the proportion of males is lower than that of the

females. This age-sex pattern is characteristic of the youthful populations of developing

countries.

18

Figure 2.1: Population pyramid

10,000 5,000 0 5,000 10,000

0-4

5-9

10-14

15-19

20-24

25-29

30-34

35-39

40-44

45-49

50-54

55-59

60-64

65-69

70-74

75-79

80-84

85+

Population

Age

FemaleMale

Source: Ghana Statistical Service, 2010 Population and Housing Census

2.3.1 Dependency ratio

The age structure of the population results in a high age dependency ratio (population under

15 years and those aged 65 years and older to those in the age group 15-64 years). The age-

dependency ratio is often used as an indicator of the economic burden that the productive

portion of a population must carry. From Table 2.4, the dependency ratio for the Municipal is

81.3. This means that 81 persons depend on 100 people in the working age group for their

livelihood.

Table 2.4: Age dependency ratio by sex

Age Group

Both Sexes Males Females

Number Percent Number Percent Number Percent

All Ages 136,483 100.0

64,028 100.0

72,455 100.0

0-14 50,029 36.7

25,468 39.8

24,561 33.9

15-64 75,289 55.2

34,557 54.0

40,732 56.2

65+ 11,165 8.2

4,003 6.3

7,162 9.9

All Ages dependency Ratio 81.3

85.3

77.9 Child dependency Ratio 66.4

73.7

60.3

Old age dependency Ratio 14.8 11.6 17.6

Source: Ghana Statistical Service, 2010 Population and Housing Census

19

2.4 Migration, Fertility and Mortality

2.4.1 Fertility

Fertility refers to the number of children born to women. Fertility is affected by cultural,

social, economic and health factors such as the proportion of women in sexual union, the

percentage of women using contraception, the level of induced abortion amongst others.

Information on fertility is crucial for district planners and others who seek to formulate

explicit policies to reduce high population growth that may adversely affect social and

economic development.

Two types of fertility measures are used to examine levels of fertility in the municipality.

These are current fertility measures and cumulative fertility measures. Current fertility

measures such as total fertility rate (TFR) are based on data covering a short period of time

such as a year (in the case of population censuses) or five years (in surveys), while

cumulative measures such as mean children ever born are based on retrospective fertility data

covering women’s reproductive performance over their lifetime. Table 2.5 presents reported

total fertility rate, general fertility rate and crude birth rate by district in the Eastern Region of

Ghana. As shown in Table 2.6, the total fertility rate for the municipality is 3.6 as compared

to the Eastern Regional average of 3.5.

Table 2.5: Reported total fertility rate, general fertility rate and crude birth rate

by district

District Population

Number

of

women

15-49

years

Number

of births

in last 12

months

Total

Fertility

Rate

General

Fertility

Rate

Crude

Birth

Rate

All Districts 2,633,154 788,455 67,900 3.5 103.9 25.8

Kwahu North 218,235 58,005 6,572 4.4 132.3 30.1

West Akim Municipal 195,349 58,024 5,434 3.9 113.8 27.8

Kwaebibirem 192,562 58,556 5,407 3.9 111.4 28.1

New Juaben Municipal 183,727 62,150 3,612 2.4 68.0 19.7

East Akim Municipal 167,896 51,132 3,807 3.1 89.9 22.7

Suhum-Kraboa Coaltar 167,551 48,762 4,317 3.6 108.0 25.8

Birim Municipal 144,869 45,720 3,411 3.1 90.1 23.5

Akwapem North 136,483 41,211 3,549 3.6 103.4 26.0

Akwapem South Municipal 123,501 38,447 3,091 3.3 95.5 25.0

Birim South 119,767 35,212 3,259 3.9 114.3 27.2

Atiwa 110,622 31,359 3,207 4.2 125.9 29.0

Fanteakwa 108,614 31,368 2,769 3.7 107.2 25.5

Asuogyaman 98,046 30,675 2,254 3.1 89.5 23.0

Akyem Mansa 97,374 27,417 2,735 4.3 122.3 28.1

Kwahu West Municipal 93,584 29,311 2,295 3.3 95.0 24.5

Lower Manya 89,246 29,546 2,190 3.0 87.4 24.5

Yilo Krobo 87,847 27,007 2,030 2.9 90.1 23.1

Birim North 78,907 22,778 2,163 3.9 116.1 27.4

Kwahu East 77,125 21,132 2,067 4.2 120.7 26.8

Upper Manya 72,092 20,332 1,859 3.7 111.5 25.8

Kwahu South 69,757 20,311 1,872 3.8 114.0 26.8 Source: Ghana Statistical Service, 2010 Population and Housing Census

20

Current fertility

A total fertility rate of 3.6 indicates that, on average, a woman in the municipality would have

about 4 children by the time she passes through her reproductive age conforming to current

age-specific fertility rates. The municipality also recorded a crude birth rate of 26.0 births per

1000 population and a general fertility rate of 103.4 births per 1000 women of reproductive

age.

Cumulative (Lifetime) fertility

The mean number of children ever born per woman measures the lifetime or cumulative

fertility performance of female respondents 12 years and older. Table 2.6 shows the children

ever born and the mean number of children ever born in the Municipality. It indicates that the

Municipality’s average completed family size was 2.7 children per woman. As expected, the

average number of children ever-born increases consistently with age of mother. The reported

mean number of children for young teenage girls (12-19 years) is less than one indicating a

low level of teenage fertility in district. Overall, the distribution of the number of children

surviving follows the same pattern as children ever born. The higher the average number of

children ever-born, the higher the survivorship. Improvement in maternal and child health

care in the district such as postnatal care, immunization and nutrition may have accounted for

the high level of child survivorship within the municipality.

Table 2.6: Female population 12 years and older by age, children ever born,

children surviving and sex of child

Age

Number

of Female

Children Ever Born Children Surviving Mean

Births

Both

Sexes Male Female

Mean

Births

Both

Sexes Male Female

All

Ages 52,624

2.702 142,181 71,850 70,331

2.337 122,983 61,399 61,584

12 - 14 4,730

0.005 23 5 18

0.004 21 5 16

15-19 6,860

0.105 723 406 317

0.097 665 363 302

20-24 5,946

0.76 4,517 2,298 2,219

0.7 4,160 2,058 2,102

25-29 5,613

1.592 8,935 4,505 4,430

1.49 8,361 4,146 4,215

30-34 4,359

2.577 11,234 5,894 5,340

2.378 10,365 5,364 5,001

35-39 3,974

3.467 13,778 7,031 6,747

3.219 12,791 6,500 6,291

40-44 3,422

4.083 13,972 7,165 6,807

3.702 12,668 6,445 6,223

45-49 3,071

4.293 13,185 6,526 6,659

3.822 11,738 5,798 5,940

50-54 3,236

4.792 15,508 7,856 7,652

4.209 13,621 6,856 6,765

55-59 2,091

4.685 9,797 4,888 4,909

4.077 8,526 4,177 4,349

60+ 9,322 5.418 50,509 25,276 25,233 4.298 40,067 19,687 20,380 Source: Ghana Statistical Service, 2010 Population and Housing Census

2.4.2 Mortality

Mortality is one of the three major components of population change. The level and pattern of

mortality is a reflection of the health status of a population. Thus, indices of mortality have

been used as indicators of socio-economic development. This section of the report examines

morality in the district.

Table 2.7 shows that a total of 1,317deaths were recorded in the municipality within the 12

months preceding the census, comprising of 698 male deaths and 619 female deaths. A small

proportion (8.0%) of all the deaths was due to accidents, violence, homicide or suicide. The

21

highest share of all deaths was among children under-one year old (14.6%), followed by

persons aged 90 years older (7.5%) and persons aged 70-75 years (7.1%). Altogether, the

elderly population (65 years and older) accounted for close to four in ten of all deaths that

occurred in the municipality. Children 5-9 years recorded the lowest proportion of deaths

during the period (1.1%). For the sexes, there were more male than female deaths among

children under-five years (21.2% versus 13.9%) and the elderly population (36.9% versus

12.9%).

Table 2.7: Household deaths (within 12 months preceding the census) by age, cause of

death and sex

Age Group

Household deaths in past

12 months

Deaths by accident,

violence, homicide or

suicide Other deaths

Both

sexes

Male

Female

Both

sexes

Male

Female Both

sexes

Male

Female

All ages

1,317 698 619

106 55 51

1,211 643 568

Under 1

192 128 64

24 14 10

168 114 54

1 - 4

40 20 20

6 1 5

34 19 15

5 - 9

15 7 8

1 - 1

14 7 7

10 - 14

24 11 13

1 1 -

23 10 13

15 - 19

22 14 8

2 2 -

20 12 8

20 - 24

35 20 15

4 3 1

31 17 14

25 - 29

39 16 23

15 7 8

24 9 15

30 - 34

59 32 27

4 2 2

55 30 25

35 - 39

65 24 41

9 3 6

56 21 35

40 - 44

64 39 25

9 6 3

55 33 22

45 - 49

49 22 27

1 - 1

48 22 26

50 - 54

76 38 38

10 4 6

66 34 32

55 - 59

71 44 27

4 4 -

67 40 27

60 - 64

56 31 25

2 2 -

54 29 25

65 - 69

58 30 28

3 3 -

55 27 28

70 - 74

92 42 49

3 2 1

89 41 48

75 - 79

94 56 38

1 - 1

93 56 37

80 - 84

111 48 63

4 - 4

107 48 59

85 - 89

56 33 23

3 1 2

53 32 21

90+ 99 42 57 - - - 99 42 57 Source: Ghana Statistical Service, 2010 Population and Housing Census

On the risk of death as a result of pregnancy, 17 out of the total of 1,317 deaths were

recorded as maternal deaths in the municipality. The highest recorded maternal deaths was

reported by age-groups 30-34 years and 35-39 years (35.3% by both age groups), followed

by age group 25-29 years (11.8%). No maternal deaths were recorded among teenage

mothers and mothers aged 55 years and older.

22

Table 2.8: Household deaths (within 12 months preceding the census) by age, sex and

female pregnancy deaths

Age Group

Household deaths in past

12 months Total

Female deaths (12-54

years)

Both

sexes

Male

Female Number Percent Pregnancy

Non-

pregnancy

All ages

1,317 698 619

213 100.0

17 196 Under 1

192 128 64

- 0.0

- -

1 - 4

40 20 20

- 0.0

- -

5 - 9

15 7 8

- 0.0

- -

10 - 14

24 11 13

9 4.2

- 9

15 - 19

22 14 8

8 3.8

- 8

20 - 24

35 20 15

15 7.0

1 14

25 - 29

39 16 23

23 10.8

2 21

30 - 34

59 32 27

27 12.7

6 21

35 - 39

65 24 41

41 19.2

6 53

40 - 44

64 39 25

25 11.7

1 24

45 - 49

49 22 27

27 12.7

- 27

50 - 54

76 38 38

38 17.8

1 37

55 - 59

71 44 27

- 0.0

- -

60 - 64

56 31 25

- 0.0

- -

65 - 69

58 30 28

- 0.0

- -

70 - 74

92 42 49

- 0.0

- -

75 - 79

94 56 38

- 0.0

- -

80 - 84

111 48 63

- 0.0

- -

85 - 89

56 33 23

- 0.0

- -

90+ 99 42 57 - 0.0 - - Source: Ghana Statistical Service, 2010 Population and Housing Census

2.4.3 Migration

Migration is the physical or geographical movement by individuals or groups of people from

one area to another or across a specified boundary for the purpose of establishing a new

permanent or semi-permanent residence. The migratory movements in the district are

examined in this section by comparing locality of birth with locality of enumeration.

Table 2.9 presents the distribution of the population by birthplace and duration of residence.

It shows that the Municipality had a total of 45,183 migrants in 2010. Out of this number

majority (58.3%) were born in other localities in the Eastern Region and 41.7 percent were

born in another region in Ghana. The largest number of migrants from other regions moved to

the district from Volta Region (4,639) followed by Greater Accra Region (4,601), Ashanti

Region (3,043). The proximity of Greater Accra and Volta regions to the Municipality may

explain why larger numbers of people moved from those regions to the municipality. The

lowest number of migrants moved from Upper West Region (164).

With regard to the duration of stay, the highest proportion of migrants had resided in the

municipality for between one and four years (30.5%), followed by those who had stayed for

more than 20 years (20.2%) and those who had stayed for 10 to 19 years (18.6%). Upper

West Region had the highest proportion of migrants who had resided in the municipality for

more than ten years (47.6%), followed closely by Volta Region (46.9%). The Greater Accra

Region recorded the lowest proportion of migrants who had resided in the municipality for

more than ten years.

23

Table 2.9: Birthplace by duration of residence of migrants

Birthplace Number

Duration of residence (%)

Less than

1 year

1-4

years

5-9

years

10-19

years

20+

years

Total 45,482 13.5 30.5 17.2 18.6 20.2

Born elsewhere in the region 26,532 12.7 29.7 17.5 19.3 20.8

Born elsewhere in another region:

Western 1,527 15.8 32.9 19.4 19.3 12.6

Central 1,895 13.8 35.2 16.1 17.0 17.8

Greater Accra 4,601 18.1 35.8 16.3 16.8 13.0

Volta 4,639 11.7 25.6 15.8 17.9 29.0

Eastern - - - - - -

Ashanti 3,043 15.1 33.3 17.5 17.1 17.1

Brong Ahafo 830 15.2 30.2 18.8 18.4 17.3

Northern 735 11.8 30.6 18.1 17.0 22.4

Upper East 612 18.6 25.5 16.8 17.5 21.6

Upper West 164 16.5 22.0 14.0 25.6 22.0

Outside Ghana 904 12.3 34.8 14.0 16.9 21.9 Source: Ghana Statistical Service, 2010 Population and Housing Census

24

CHAPTER THREE

SOCIAL CHARACTERISTICS

3.1 Introduction

Data on the social characteristics of the population in the Municipality is important for

planning social services such as education, housing, health care, and social welfare. This

chapter assesses the social characteristics of the population in the Akwapem North

Municipality. The areas covered in the chapter include household characteristics, marital

status, nationality, religious affiliation and literacy and education.

3.2 Household size, composition and structure

The household is the basic unit by which the characteristics and demographic elements of a

population can be understood. The size, structure and composition of a household are

influenced by factors such as the society, economy and certain demographic variables.

The 2010 Population and Housing Census define a household as a person or group of persons

who lived together in the same house or compound and share the same house-keeping

arrangements and constitute a single consumption unit.

3.2.1 Household size

The Akwapem Municipality has a total of 33,322 households and household population of

134,359. The majority (68.1%) of the households in the Municipality are headed by males

with 38.1 percent headed by females. The average household size for the district is 4.0

persons per household. It is observed that the average household size is higher in female-

headed households than male-headed households (4.8 against 3.4). The distribution further shows that one-member households constituted the highest proportion of the total number of

households in the district (20.4%).

Table 3.1: Household size by sex of household head

Household Size

Both Sexes Male Head Female Head Number Percent Number Percent Number Percent

Total Household Population 134,359 100.0

63,021 100.0

71,338 100.0

Total Households 33,322 100.0

18,381 100.0

14,941 100.0

1 6,800 20.4

4,041 22.0

2,759 18.5

2 4,696 14.1

2,053 11.2

2,643 17.7

3 4,668 14.0

2,165 11.8

2,503 16.8

4 4,663 14.0

2,467 13.4

2,196 14.7

5 4,070 12.2

2,446 13.3

1,624 10.9

6 3,016 9.1

1,820 9.9

1,196 8.0

7 1,932 5.8

1,224 6.7

708 4.7

8 1,283 3.9

828 4.5

455 3.0

9 810 2.4

486 2.6

324 2.2

10+ 1,384 4.2

851 4.6

533 3.6

Average household size 4 3.4 4.8 Source: Ghana Statistical Service, 2010 Population and Housing Census

25

There are equal proportions of households composed of two, three, and four members, each

recording 14.0 percent. Household with nine members recorded the lowest proportion

(2.4%). Slightly more than one in ten (10.5%) households had eight members or more. The

proportion of households headed by males is relatively higher than those headed by females

in single person households (22.0% versus 18.5%) and in all the other households, with the

exception of households with two to four household members.

3.2.2 Household composition

The household normally consists of a head, with or without spouse, children, in-laws, parents,

grandchildren, and other relatives. Data on household population by composition and sex is

presented in Table 3.2. About a fifth (24.8%) of all household members constituted

household heads, and almost a tenth (9.1%) were spouses (Table 3.2). A little more than one

third of all household members are children (38.0%), with grandchildren constituting 12.5

percent of all household members. Though the proportions of other relatives (7.0%), siblings

(4.0%) and parents/parents-in-law (1.1%) are relatively low, they are indications of the fact

that the extended family living arrangements still persist in the district. Adopted

child/children and sons/daughters-laws constitute very low proportions of household

members (0.4% and 0.6% respectively).

Table 3.2: Household composition by sex

Household composition

Total Male Female

Number Percent Number Percent Number Percent

Total 134,359 100.0

63,021 100.0

71,338 100.0

Head 33,322 24.8

18,381 29.2

14,941 20.9

Spouse (wife/husband) 12,256 9.1

1,167 1.9

11,089 15.5

Child (son/daughter) 51,063 38.0

25,795 40.9

25,268 35.4

Parent/parent in-law 1,423 1.1

215 0.3

1,208 1.7

Son/daughter in-law 857 0.6

248 0.4

609 0.9

Grandchild 16,831 12.5

8,483 13.5

8,348 11.7

Brother/sister 5,333 4.0

2,549 4.0

2,784 3.9

Step child 1,002 0.7

522 0.8

480 0.7

Adopted/foster child 479 0.4

207 0.3

272 0.4

Other relative 9,376 7.0

4,226 6.7

5,150 7.2

Non-relative 2,417 1.8 1,228 1.9 1,189 1.7 Source: Ghana Statistical Service, 2010 Population and Housing Census

For the sexes, there are more males than females who were enumerated as heads of

households (29.2% versus 20.9%) and also more male children (40.9%) than female children.

Female spouses constitute a higher percentage (15.5%) compared to male spouses (1.9%).

This gender characteristic of household composition is expected since a male would normally

have his wife/wives or sexual partner(s) co-resident. Males do not normally reside with a

female head in the District and in elsewhere in Ghana.

3.2.3 Household structure

Household structure refers to the type of relationship (whether related or unrelated) among

household members. Table 3.3 shows that single person households constituted 5.1 percent of

all households. Households composed of head, his or her spouse and children constituted the

highest proportion of households (24.3%), followed by households with the single parent

extended structure (21.2%) and households made up of head, spouse, children and relative of

26

head (18.0%). Single parent extended with non-relative households constituted the lowest

proportion (1.6%).

Table 3.3: Household population by structure and sex

Household structure

Number Percent

Total Male Female Total Male Female

Total 134,359 63,021 71,338

100.0 100.0 100.0

Head only 6,800 4,041 2,759

5.1 6.4 3.9

Head and a spouse only 2,026 1,015 1,011

1.5 1.6 1.4

Nuclear (Head, spouse(s) and children) 32,603 16,784 15,819

24.3 26.6 22.2

Extended (Head, spouse(s), children and Head's

relatives) 24,120 11,939 12,181

18.0 18.9 17.1

Extended + non relatives 1,886 944 942

1.4 1.5 1.3

Head spouse(s) and other composition 5,260 2,611 2,649

3.9 4.1 3.7

Single parent Nuclear 15,263 6,493 8,770

11.4 10.3 12.3

Single parent Extended 28,424 11,364 17,060

21.2 18.0 23.9

Single parent Extended + non relative 2,188 908 1,280

1.6 1.4 1.8

Head and other composition but no spouse 15,789 6,922 8,867 11.8 11.0 12.4

Source: Ghana Statistical Service, 2010 Population and Housing Census

3.3 Marital Status

Marriage is one characteristic of population that is regulated by social, economic, biological,

legal and religious factors. Marriage is socially defined as legally, religiously or traditionally

recognized union entered into by a man and woman usually with intention of living together

and having sexual relations and entailing property and inheritance rights. Marriage is

associated with population dynamics as it affects the processes and levels of fertility,

mortality and migration.

3.3.1 Marital status

Table 3.4 presents information on persons 12 years and older by sex, age and marital status.

Slightly more than 2 out of every 5 (40.9 percent) of the population had never married, 38.8

percent were married and 4.0 percent were in informal/consensual union as can be seen from

Table 3.4. In addition, 16.3 percent had once been married but are separated (3.3%), divorced

(5.1% or widowed (7.9%).

3.3.2 Marital status by sex

For the sexes, it can be seen from Table 3.4 that slightly more males and females are married

(39.2% versus 38.4%). However, more females than males are separated (5.4% versus 2.4%)

and divorced (6.8% versus 3%). It can be further observed that a higher proportion of males

than females had never been married (49.4% versus 33.5%), indicating that males are more

likely than females to postpone marriage in the municipality.

3.3.3 Marital status by age

The distribution of marital status by age in Table 3.4 indicates that in general, the proportion

never married is higher among adolescents (12-19 years) and young adults (20-24 years).

This may be a reflection of the impact of schooling and training of these young cohorts.

Overall, the percentage of the married population increases rapidly from age-group 20-24

years (23.8%) and reaches a peak at age cohort 35- 44 years and starts to decrease through to

age-group 65 years and older. As expected, the widowed population increases with increase

27

in age, with the age groups 60-64 and 65+ years recording the highest proportions of 21.8

percent and 36.8 percent respectively.

Table 3.4: Persons 12 years and older by sex, age-group and marital status

Sex/Age-

group

Never

married

Informal/

consensual

union/living

together Married Separated Divorced Widowed

Total

Number Percent

Both Sexes

Total 96,015 100.0 40.9 4.0 38.8 3.3 5.1 7.9

12 - 14 9,561 100.0 95.4 0.4 4.2 0.0 0.0 0.0

15 - 19 14,184 100.0 93.1 1.4 5.0 0.3 0.1 0.1

20 - 24 11,228 100.0 71.8 6.2 19.9 1.1 0.8 0.2

25 - 29 9,928 100.0 42.7 9.6 43.0 2.3 1.9 0.5

30 - 34 8,194 100.0 22.8 7.5 61.6 3.2 3.6 1.3

35 - 39 7,295 100.0 12.9 6.1 69.3 4.1 5.6 1.9

40 - 44 6,190 100.0 8.8 4.4 69.3 5.9 7.7 3.9

45 - 49 5,468 100.0 6.0 3.9 67.8 6.3 10.5 5.5

50 - 54 5,389 100.0 5.3 2.7 61.3 8.3 12.3 10.1

55 - 59 3,672 100.0 3.6 2.3 59.4 6.9 14.1 13.7

60 - 64 3,741 100.0 3.6 1.8 52.6 7.2 13.0 21.8

65+ 11,165 100.0 3.5 1.1 36.2 5.2 10.6 43.4

Male

Total 43,391 100.0 49.4 3.6 39.2 2.4 3.0 2.4

12 - 14 4,831 100.0 95.0 0.3 4.7 0.0 0.0 0.0

15 - 19 7,324 100.0 95.6 0.5 3.6 0.2 0.1 0.1

20 - 24 5,282 100.0 87.7 2.6 8.9 0.5 0.2 0.2

25 - 29 4,315 100.0 57.2 8.3 32.1 1.2 0.9 0.3

30 - 34 3,835 100.0 32.3 8.3 54.4 2.2 2.0 0.7

35 - 39 3,321 100.0 17.1 7.1 68.7 2.9 3.3 0.9

40 - 44 2,768 100.0 10.7 4.9 73.7 4.0 4.9 1.7

45 - 49 2,397 100.0 6.8 4.5 76.1 5.0 5.8 1.8

50 - 54 2,153 100.0 7.0 3.0 72.9 7.2 7.2 2.7

55 - 59 1,581 100.0 3.9 3.1 75.4 4.2 9.2 4.2

60 - 64 1,581 100.0 4.3 2.7 72.6 5.9 8.7 5.7

65+ 4,003 100.0 4.6 1.6 63.1 5.4 9.2 16.0

Female

Total 52,624 100.0 33.9 4.3 38.4 4.1 6.8 12.5

12 - 14 4,730 100.0 95.8 0.5 3.7 0.0 0.0 0.0

15 - 19 6,860 100.0 90.3 2.4 6.6 0.5 0.1 0.1

20 - 24 5,946 100.0 57.7 9.4 29.6 1.6 1.4 0.2

25 - 29 5,613 100.0 31.5 10.6 51.4 3.2 2.7 0.6

30 - 34 4,359 100.0 14.5 6.7 68.0 4.0 5.0 1.8

35 - 39 3,974 100.0 9.4 5.2 69.9 5.2 7.6 2.7

40 - 44 3,422 100.0 7.2 4.1 65.8 7.4 9.9 5.6

45 - 49 3,071 100.0 5.4 3.4 61.3 7.3 14.1 8.4

50 - 54 3,236 100.0 4.1 2.6 53.6 9.0 15.7 15.0

55 - 59 2,091 100.0 3.3 1.7 47.3 8.9 17.8 20.9

60 - 64 2,160 100.0 3.0 1.2 37.9 8.1 16.2 33.6

65+ 7,162 100.0 2.9 0.8 21.1 5.0 11.3 58.8 Source: Ghana Statistical Service, 2010 Population and Housing Census

3.3.4 Marital status and level of education

Table 3.5 shows the distribution of persons 12 years and older by sex, marital status and level

of education. With the exception of the widowed population, all the other categories of

marital statuses recorded proportions above 50 percent among those with basic level of

28

education; and those in informal/consensual union constituted the highest percentage

(69.7%). Among persons with no education slightly more than half (50.2%) are widowed, a

quarter (24.5%) approximately are divorced and very slightly over a fifth (22.1%) are

separated. At the secondary school level, the never married recorded the highest percentage

(18.6%), followed by those in informal union (11.6%), while the widowed recorded the

lowest percentage (2.8%). Each of the marital status categories recorded proportions less than

four percent at the tertiary level of education. Similar pattern can be observed for the sexes.

Table 3.5: Persons 12 years and older by sex, marital status and level of education

Sex/Marital status Number

All

levels

No

Education Basic1

Secon-

dary2

Vocational/

technical/

commercial

Post

middle/

secondary

certificate/

diploma3 Tertiary4

Both Sexes

Total 96,015 100.0 16.0 63.0 12.0 2.3 4.3 2.4 Never married 39,249 100.0 5.0 68.6 18.6 1.5 4.1 2.1

Informal/consensual

union/living together 3,848 100.0 11.6 69.7 11.6 3.0 3.0 1.1

Married 37,215 100.0 19.4 61.0 8.2 3.1 5.0 3.4

Separated 3,214 100.0 22.1 62.7 7.2 3.4 3.4 1.1

Divorced 4,903 100.0 24.5 62.3 5.9 2.9 3.2 1.1

Widowed 7,586 100.0 50.2 40.7 2.8 1.7 4.1 0.5

Male

Total 43,391 100.0 9.5 64.7 14.8 2.4 5.4 3.4 Never married 21,428 100.0 4.6 68.3 19.0 1.2 4.5 2.4

Informal/consensual

union/living together 1,564 100.0 7.4 68.4 15.0 2.6 4.7 1.9

Married 17,008 100.0 13.5 60.6 10.6 3.7 6.5 5.1

Separated 1,036 100.0 15.1 63.8 11.1 3.5 4.2 2.3

Divorced 1,323 100.0 17.8 64.6 8.7 2.9 3.9 2.3

Widowed 1,032 100.0 30.4 50.7 7.8 2.9 7.2 1.1

Female

Total 52,624 100.0 21.3 61.6 9.8 2.3 3.5 1.5 Never married 17,821 100.0 5.5 68.9 18.3 1.9 3.6 1.8

Informal/consensual

union/living together 2,284 100.0 14.5 70.6 9.2 3.3 1.8 0.5

Married 20,207 100.0 24.3 61.4 6.2 2.6 3.6 1.9

Separated 2,178 100.0 25.4 62.2 5.4 3.4 3.0 0.6

Divorced 3,580 100.0 27.0 61.5 4.9 2.9 3.0 0.6

Widowed 6,554 100.0 53.3 39.1 2.0 1.5 3.6 0.4

Source: Ghana Statistical Service, 2010 Population and Housing Census

1 Basic: Primary, Middle and JSS/JHS 2 Secondary: SSS/SHS and Secondary 3 Post Middle/ Sec. Cert./Diploma: Teacher training/ College of education, Agric., Nursing , University Diploma, HND etc. 4 Tertiary: Bachelor’s Degree and Post Graduate or higher

3.3.4 Sex, marital status and activity status

Activity status of persons provides an indication of ability to marry and support a spouse

financially in some cultures in Ghana. Table 3.6 shows the distribution of persons 12 years

and older by marital status, economic activity status and sex. Majority of those who have

never married are not economically active (60.7%), followed by those who are widowed

(57.6%). Apart from the population that have never married and the widowed, more than half

29

of the population in the remaining marital categories are employed, with those married

recording the highest (78.7%). The same pattern is observed for the sexes.

Table 3.6: Persons 12 years and older by sex, marital status and economic

activity status

Sex/Marital Status

Total Employed Unemployed Economically not

active

Number Percent Number Percent Number Percent Number Percent

Both Sexes

Total 96,015 100.0

53,589 55.9

4,650 4.8

37,776 39.3

Never married 39,249 100.0

12,854 32.7

2,566 6.5

23,829 60.8

Informal/consensual

union/living together 3,848 100.0

2,890 75.1

280 7.3

678 17.6

Married 37,215 100.0

29,300 78.7

1,327 3.6

6,588 17.7

Separated 3,214 100.0

2,132 66.3

167 5.2

915 28.5

Divorced 4,903 100.0

3,308 67.5

196 4.0

1,399 28.5

Widowed 7,586 100.0

3,105 40.9

114 1.5

4,367 57.6

Male

Total 43,391 100.0

24,642 56.8

2,065 4.7

16,684 38.5

Never married 21,428 100.0

7,520 35.1

1,437 6.7

12,471 58.2

Informal/consensual

union/living together 1,564 100.0

1,298 83.0

86 5.5

180 11.5

Married 17,008 100.0

13,785 81.1

431 2.5

2,792 16.4

Separated 1,036 100.0

698 67.4

44 4.2

294 28.4

Divorced 1,323 100.0

891 67.3

45 3.4

387 29.3

Widowed 1,032 100.0

450 43.6

22 2.1

560 54.3

Female

Total 52,624 100.0

28,947 55.0

2,585 4.9

21,092 40.1

Never married 17,821 100.0

5,334 29.9

1,129 6.3

11,358 63.8

Informal/consensual

union/living together 2,284 100.0

1,592 69.7

194 8.5

498 21.8

Married 20,207 100.0

15,515 76.8

896 4.4

3,796 18.8

Separated 2,178 100.0

1,434 65.9

123 5.6

621 28.5

Divorced 3,580 100.0

2,417 67.5

151 4.2

1,012 28.3

Widowed 6,554 100.0 2,655 40.5 92 1.4 3,807 58.1

Source: Ghana Statistical Service, 2010 Population and Housing Census

3.4 Nationality

Nationality is defined as the country to which a person belongs or has citizenship.

Information on nationality was captured in the 2010 PHC to differentiate between a Ghanaian

by birth, Ghanaian by dual nationality and a Ghanaian by naturalization as well as all other

nationals. Other nationals were grouped into ECOWAS nationals, Africans not from

ECOWAS countries and non-Africans.

30

Table 3.7: Population by nationality and sex

Nationality

Both sexes

Male

Female

Number Percent Number Percent Number Percent

Total 136,483 100.0

64,028 100.0

72,455 100.0

Ghanaian by birth 133,090 97.5

62,398 97.5

70,692 97.6

Dual Nationality 1,626 1.2

779 1.2

847 1.2

Ghanaian by naturalisation 441 0.3

215 0.3

226 0.3

ECOWAS 762 0.6

395 0.6

367 0.5

Africa other than ECOWAS 297 0.2

134 0.2

163 0.2

Other 267 0.2 107 0.2 160 0.2 Source: Ghana Statistical Service, 2010 Population and Housing Census

The information on nationality and by sex in the district is presented in Table 3.7. Majority

of the population in the Municipality are Ghanaians by birth (97.5%). Ghanaians with dual

nationality constitute 1.2 percent and Ghanaians by naturalization form less than one percent

(0.3%). The rest of the population, ECOWAS nationals, Africans other than ECOWAS and

non-Africans constitute only one percent of the population. A similar distributional pattern of

the population by nationality is observed for the sexes.

3.5 Religious affiliations

Table 3.8 shows the distribution of the population by religious affiliation and sex. Christians

constitute the majority (88.9%) of the population in the municipality, followed by Moslems

(2.2 %) and adherents of traditional religion (1.9%). Persons who indicated that they have no

religious affiliation constitute about 6.0 percent. Protestants constitute the highest percentage

(41.1%) of the Christian population in the Municipality, followed by Pentecostal/Charismatic

(33.5%). Catholics recorded the lowest proportion of Christians in the district (3.1%).

For the sexes, 91.1 percent of the females compared to 86.6 percent of the males were

Christians while Moslems constituted 2.6 percent of males and 1.9 percent of females. Those

who have no religion comprised 7.7 percent of males and 4.4 percent of females.

Table 3.8: Population by religion and sex

Religion

Both sexes

Male

Female

Number Percent Number Percent Number Percent

Total 136,483 100.0

64,028 100.0

72,455 100.0

No Religion 8,110 5.9

4,899 7.7

3,211 4.4

Catholic 4,283 3.1

2,209 3.5

2,074 2.9

Protestant (Anglican Lutheran etc.) 56,111 41.1

25,488 39.8

30,623 42.3

Pentecostal/Charismatic 45,735 33.5

20,644 32.2

25,091 34.6

Other Christians 15,310 11.2

7,131 11.1

8,179 11.3

Islam 3,029 2.2

1,667 2.6

1,362 1.9

Traditionalist 2,533 1.9

1,358 2.1

1,175 1.6

Other (Specify) 1,372 1.0 632 1.0 740 1.0 Source: Ghana Statistical Service, 2010 Population and Housing Census

31

3.6 Literacy and Education

Education is one of the indicators of human development. Since independence, more

investments have been made in the educational sector to ensure that every child of school

going age has access to education. An important initiative by the Government of Ghana is the

‘Free Compulsory Universal Basic Education” (FCUBE) aimed at increasing access to basic

education. Adult literacy programmes have also been implemented over the years to improve

literacy status of the adult population.

3.6.1 Literacy

Literacy is measured by the ability to read and write in any language with understanding. The

ability to read and write is essential for the population and the nation, as literacy not only

enables people to access information on what goes on in all spheres of life, but also enhances

vertical social mobility in.

Table 3.9 shows that 84.4 percent of the population 11 years and older in the municipality are

literate. The level of literacy is higher for males (90.1%) than for females (79.1%). A highest

percentage (70.2%) of the population in the municipality are literate in both English and a

Ghanaian language, 17.6 percent are literate in English only, and 11.4 percent are literate in

Ghanaian language only. Less than one percent of the population can read and understand

English, French and a Ghanaian language (0.7%).

With regard to literacy status by age and sex, Table 3.9 shows that the proportion of both

males and females who were literate in English only is higher for the younger cohorts

compared to the older cohorts. This pattern may reflect the impact of Ghana Government’s

policy of increasing access to education at all levels. On the other hand, the proportion that is

literate in Ghanaian language only is higher among both male and female older cohorts of the

population than the younger cohorts. Again this may be as a result of Adult Literacy

Programmes in Ghanaian language in the Municipality. The proportions recorded for literacy

in English and Ghanaian languages only for both males and females in all the age cohorts

ranged from 66.4 percent to 75.9 percent but the figures for males are generally higher than

those for females in all the age categories. Overall, an insignificant proportion (1.1% or less)

is literate in English and French and English, French and a Ghanaian language.

32

Table 3.9: Population 11 years and older by sex, age and literacy status

Sex/Age Group

None

(not

literate) Literate

Total

Percent

English

only

Ghanaian

language

only

English

and

Ghanaian

language

English

and

French

English,

French and

Ghanaian

language

Both Sexes

Total 15,435 83,388 100.0 17.6 11.4 70.2 0.2 0.7

11 - 14 271 12,098 100.0 26.1 6.8 66.4 0.1 0.6

15 - 19 448 13,736 100.0 17.7 5.4 75.9 0.1 0.9

20 - 24 814 10,414 100.0 18.2 7.5 73.3 0.3 0.8

25 - 29 1,024 8,904 100.0 19.4 10.8 68.9 0.2 0.7

30 - 34 1,112 7,082 100.0 18.4 14.3 66.3 0.3 0.6

35 - 39 1,215 6,080 100.0 17.0 15.2 66.8 0.1 0.8

40 - 44 1,110 5,080 100.0 16.0 15.6 67.7 0.1 0.7

45 - 49 1,009 4,459 100.0 14.2 16.6 68.4 0.2 0.6

50 - 54 1,117 4,272 100.0 12.3 15.1 71.6 0.3 0.7

55 - 59 728 2,944 100.0 12.4 15.2 71.8 0.2 0.4

60 - 64 1,070 2,671 100.0 10.8 16.1 72.8 0.1 0.2

65+ 5,517 5,648 100.0 8.5 21.6 69.2 0.1 0.6

Male

Total 4,150 40,626 100.0 17.0 8.4 73.7 0.2 0.7

11 - 14 136 6,080 100.0 26.9 6.0 66.7 0.1 0.4

15 - 19 198 7,126 100.0 17.4 5.1 76.8 0.1 0.6

20 - 24 282 5,000 100.0 15.9 5.6 77.4 0.3 0.8

25 - 29 313 4,002 100.0 17.5 8.7 72.9 0.2 0.7

30 - 34 369 3,466 100.0 17.7 10.3 70.7 0.5 0.8

35 - 39 364 2,957 100.0 16.2 12.2 70.3 0.3 1.0

40 - 44 322 2,446 100.0 15.5 10.5 72.7 0.1 1.1

45 - 49 245 2,152 100.0 14.3 11.3 73.3 0.2 0.9

50 - 54 264 1,889 100.0 11.4 10.2 77.4 0.2 0.8

55 - 59 201 1,380 100.0 12.4 9.9 77.0 0.2 0.4

60 - 64 245 1,336 100.0 10.5 10.4 78.7 0.1 0.2

65+ 1,211 2,792 100.0 8.0 13.4 77.4 0.1 1.1

Female

Total 11,285 42,762 100.0 18.1 14.3 66.8 0.2 0.7

11 - 14 135 6,018 100.0 25.2 7.7 66.1 0.1 0.8

15 - 19 250 6,610 100.0 18.0 5.8 74.9 0.2 1.1

20 - 24 532 5,414 100.0 20.3 9.1 69.5 0.2 0.8

25 - 29 711 4,902 100.0 21.0 12.5 65.7 0.1 0.7

30 - 34 743 3,616 100.0 19.1 18.2 62.1 0.2 0.4

35 - 39 851 3,123 100.0 17.8 18.0 63.6 0.0 0.6

40 - 44 788 2,634 100.0 16.4 20.3 63.0 0.0 0.3

45 - 49 764 2,307 100.0 14.0 21.5 63.9 0.2 0.3

50 - 54 853 2,383 100.0 13.0 19.0 66.9 0.4 0.7

55 - 59 527 1,564 100.0 12.3 19.9 67.2 0.3 0.3

60 - 64 825 1,335 100.0 11.1 21.9 66.9 0.0 0.1

65+ 4,306 2,856 100.0 9.0 29.6 61.1 0.1 0.2

Source: Ghana Statistical Service, 2010 Population and Housing Census

33

3.6.4 Education

Information on the population 3 years and older by level of education, school attendance and

sex is presented in Table 3.10. In 2010, 25,506 persons 3 years and older in the district were

attending school at the time of the census. Of those who were attending school, majority

(87.4%) were attending school at the basic level of education (nursery, kindergarten, primary,

and JHS) and 8.5 percent were at the Senior High School level. Only 2.9 percent were

attending school at the tertiary level and less than one percent (0.4%) was attending

vocational/technical/commercial schools. Similar proportions of males and females were

attending school at the basic and secondary levels of education. However, slightly more

males than females were benefiting from tertiary education (2.9% against 2.5%).

As can be seen from Table 3.10, close to half (47.6%) of the population currently in school

are in primary schools, with about 19 percent in Junior High Schools. Less than one percent

(0.4%) of the population currently in school are in vocational/technical/commercial schools.

The population in tertiary level constitutes less than three percent (2.7%) of the population

currently in school. A similar pattern is observed for both males and females.

Overall, past school attendance follows similar pattern as current school attendance; majority

of individuals had achieved basic education (75.5%), For the sexes, a higher proportion of

males than females had achieved secondary/SHS education (5.4% versus 3.0%) and tertiary

levels of education (8.3% versus 3.7%), whereas a higher proportion of females than males

had achieved basic education (79.9% versus 70.7%). This observation suggests that more

females than males in the district tend to discontinue their education after the basic level of

education.

34

Table 3.10: Population 3 years and older by level of education, school attendance

and sex

Currently attending

Attended in the past

Both sexes

Male

Female

Both sexes

Male

Female

Level of education Number Percent Number Percent Number Percent Number Percent Number Percent Number Percent

Total 48,855 100

25,506 100

23,349 100

58,516 100

27,374 100

31,142 100

Nursery 2,502 5.1

1,249 4.9

1,253 5.4

- - - - - -

Kindergarten 7,914 16.2

4,159 16.3

3,755 16.1

- - - - - -

Primary 23,273 47.6

11,911 46.7

11,362 48.7

11,192 19.1

4,011 14.7

7,181 23.1

JSS/JHS 9,253 18.9

4,979 19.5

4,274 18.3

15,471 26.4

7,030 25.7

8,441 27.1

Middle - - - - - - 17,556 30

8,307 30.3

9,249 29.7

SSS/SHS 4,162 8.5

2,259 8.9

1,903 8.2

4,983 8.5

2,668 9.7

2,315 7.4

Secondary - - - - - - 2,397 4.1

1,477 5.4

920 3.0

Vocational/technical/

commercial 197 0.4

83 0.3

114 0.5

2,046 3.5

939 3.4

1,107 3.6

Post middle/secondary

certificate 218 0.4

114 0.4

104 0.4

1,456 2.5

683 2.5

773 2.5

Tertiary 1,336 2.7 752 2.9 584 2.5 3,415 5.8 2,259 8.3 1,156 3.7

Source: Ghana Statistical Service, 2010 Population and Housing Census

35

CHAPTER FOUR

ECONOMIC CHARACTERISTICS

4.1 Introduction

The overall socio-economic development of the Akwapem North Municipality depends on

the production of goods and services. Critical to the production process is human capital of

the district, specifically the section of the working or employed population. This chapter

examines the economic characteristics of the population with regard to activity status,

occupation, industry, sector of employment and employment status of the employed

population.

4.2 Economic Activity Status

The economically active population consists of those who worked or had a job but did not

work or were unemployed at the time of the census. The “not economically active” were

those who did home duties (household chores), were in full time education,

pensioners/retired, disabled/sick, and those who were too old/too young and those classified

as others.

4.2.1 Economic activity status

Table 4.1 presents the distribution of the population 15 years and older by economic activity

status and sex. Majority (66.1%) of the population are economically active, 33.9 percent are

economically not active. Of the economically active population, the majority (91.9%) are

employed. The economically not active population comprised mostly of persons in full time

education (41.3%) and those too old/young (18.8%) to work.

Table 4.1: Activity status of persons 15 years and older by sex

Activity status

Total Male Female

Number Percent Number Percent Number Percent

Total 86,454 100.0

38,560 100.0

47,894 100.0

Economically active 57,108 66.1

26,108 67.7

31,000 64.7

Employed 52,480 91.9

24,053 92.1

28,427 91.7

Worked 49,037 93.4

22,713 94.4

26,324 92.6

Did not work but had job to go back to 3,256 6.2

1,243 5.2

2,013 7.1

Did voluntary work without pay 187 0.4

97 0.4

90 0.3

Unemployed 4,628 8.1

2,055 7.9

2,573 8.3

Worked before, seeking work and

available 1,745 37.7

686 33.4

1,059 41.2

Seeking work for the first time and

available 2,883 62.3

1,369 66.6

1,514 58.8

Economically not active 29,346 33.9

12,452 32.3

16,894 35.3

Did home duties (household chore) 5,230 17.8

1,264 10.2

3,966 23.5

Full time education 12,119 41.3

6,749 54.2

5,370 31.8

Pensioner/Retired 1,956 6.7

1,213 9.7

743 4.4

Disabled/Sick 2,183 7.4

835 6.7

1,348 8.0

Too old/young 5,515 18.8

1,354 10.9

4,161 24.6

Other 2,343 8.0 1,037 8.3 1,306 7.7

Source: Ghana Statistical Service, 2010 Population and Housing Census

36

A slightly higher percentage of males (67.7%) than females (64.7%) are economically active.

Among the economically active, slightly more males (92.1%) than females (91.7%) are

employed. In contrast, a higher percentage (35.3) of females than males (32.3%) are not

economically active. More males (54.2%) than females (31.8%) cited full-time education

and pension/retirement (9.7% versus 4.4%) as reasons for their economic inactivity. On the

other hand, a higher proportion of females than males cited the following reasons: home

duties (23.5% versus 10.2%), disability/sickness (8.0% versus 6.7%) and being too old/too

young (24.6% versus 10.9%).

4.2.2 Economic activity status and age

Economic activity by age indicates that, young adults (20-29 years) constitute more than a

quarter (24.8%) of the employed population, and approximately another quarter (24.6%) of

adults in the age cohorts 30-39 years are also employed. As expected, adolescents (15-19

years) and young adults (20-29 years) together constituted majority (64.3%) of the

unemployed population. The reason for the unemployment status of this young people is that

most of them may still be in school or receiving training. Similarly, and for the same reason,

adolescents (15-19 years) had the highest proportion (36.8%) of the not economically active,

followed by the age cohort 20-24 years (14.3 percent). A similar pattern of this age and sex

distribution of activity status can be observed for the sexes.

Table 4.2: Population 15 years and older by sex, age and activity status

Age

group

All Status Employed Unemployed

Economically Not

Active

Number Percent Number Percent Number Percent Number Percent

Total 86,454 100.0

52,480 100.0

4,628 100.0

29,346 100.0

15 - 19 14,184 16.4

2,770 5.3

614 13.3

10,800 36.8

20 - 24 11,228 13.0

5,608 10.7

1,419 30.7

4,201 14.3

25 - 29 9,928 11.5

7,413 14.1

940 20.3

1,575 5.4

30 - 34 8,194 9.5

6,777 12.9

492 10.6

925 3.2

35 - 39 7,295 8.4

6,124 11.7

370 8.0

801 2.7

40 - 44 6,190 7.2

5,335 10.2

216 4.7

639 2.2

45 - 49 5,468 6.3

4,722 9.0

162 3.5

584 2.0

50 - 54 5,389 6.2

4,446 8.5

138 3.0

805 2.7

55 - 59 3,672 4.2

2,902 5.5

93 2.0

677 2.3

60 - 64 3,741 4.3

2,286 4.4

91 2.0

1,364 4.6

65+ 11,165 12.9

4,097 7.8

93 2.0

6,975 23.8

Male

Total 38,560 100.0

24,053 100.0

2,055 100.0

12,452 100.0

15 - 19 7,324 19.0

1,489 6.2

250 12.2

5,585 44.9

20 - 24 5,282 13.7

2,544 10.6

635 30.9

2,103 16.9

25 - 29 4,315 11.2

3,282 13.6

430 20.9

603 4.8

30 - 34 3,835 9.9

3,233 13.4

258 12.6

344 2.8

35 - 39 3,321 8.6

2,901 12.1

150 7.3

270 2.2

40 - 44 2,768 7.2

2,456 10.2

102 5.0

210 1.7

45 - 49 2,397 6.2

2,166 9.0

60 2.9

171 1.4

50 - 54 2,153 5.6

1,865 7.8

53 2.6

235 1.9

55 - 59 1,581 4.1

1,326 5.5

39 1.9

216 1.7

60 - 64 1,581 4.1

1,010 4.2

41 2.0

530 4.3

65+ 4,003 10.4

1,781 7.4

37 1.8

2,185 17.5

37

Table 4.2: Population 15 years and older by sex, age and activity status (cont’d)

Age

group

All Status Employed Unemployed

Economically Not

Active

Number Percent Number Percent Number Percent Number Percent

Female

Total 47,894 100.0

28,427 100.0

2,573 100.0

16,894 100.0

15 - 19 6,860 14.3

1,281 4.5

364 14.1

5,215 30.9

20 - 24 5,946 12.4

3,064 10.8

784 30.5

2,098 12.4

25 - 29 5,613 11.7

4,131 14.5

510 19.8

972 5.8

30 - 34 4,359 9.1

3,544 12.5

234 9.1

581 3.4

35 - 39 3,974 8.3

3,223 11.3

220 8.6

531 3.1

40 - 44 3,422 7.1

2,879 10.1

114 4.4

429 2.5

45 - 49 3,071 6.4

2,556 9.0

102 4.0

413 2.4

50 - 54 3,236 6.8

2,581 9.1

85 3.3

570 3.4

55 - 59 2,091 4.4

1,576 5.5

54 2.1

461 2.7

60 - 64 2,160 4.5

1,276 4.5

50 1.9

834 4.9

65+ 7,162 15.0 2,316 8.1 56 2.2 4,790 28.4

Source: Ghana Statistical Service, 2010 Population and Housing Census

4.3 Occupation

Occupation refers to the type of work a person was engaged in at the establishment where

he/she worked at the time of the census. Table 4.3 presents the employed population 15 years

and older by occupation and sex. As can be seen from Table 4.3, 37.0 percent of workers in

the municipality are engaged as skilled agricultural, forestry, and fisheries workers, 22.1

percent were engaged as service and sales workers and 17.8 percent are craft and related trade

workers. Occupations classified as other accounted for less than one percent (0.1%) of

workers. For the sexes, the males recorded a higher percentage than females for agricultural

forestry and fisheries occupations (41.1% against 30.2%), whereas the proportion of females

in the service and sales occupations is much higher than that of males (33.8% against 8.2%).

Table 4.3: Employed population 15 years and older by occupation and sex

Occupation

Both sexes

Male

Female

Number Percent Number Percent Number Percent

Total 52,480 100.0

24,053 100.0

28,427 100.0

Managers 1,975 3.8

800 3.3

1,175 4.1

Professionals 3,876 7.4

2,063 8.6

1,813 6.4

Technicians and associate professionals 1,061 2.0

749 3.1

312 1.1

Clerical support workers 620 1.2

391 1.6

229 0.8

Service and sales workers 11,588 22.1

1,975 8.2

9,613 33.8

Skilled agricultural forestry and fishery

workers 19,427 37.0

10,840 45.1

8,587 30.2

Craft and related trades workers 9,354 17.8

4,264 17.7

5,090 17.9

Plant and machine operators and assemblers 2,200 4.2

2,115 8.8

85 0.3

Elementary occupations 2,345 4.5

829 3.4

1,516 5.3

Other occupations 34 0.1 27 0.1 7 0.0

Source: Ghana Statistical Service, 2010 Population and Housing Census

4.4 Industry

Industry refers to the type of product produced or services rendered at the workplace of the

worker. Information was collected only on the main product produced or service rendered in

the establishment during the reference period.

38

Table 4.4 shows employed population 15 years and older by industry and sex. Three major

industries could be identified in the municipality. These, in order of importance, are

agriculture, forestry and fishing (37.4%); wholesale and retail, repairs of motor vehicles and

motorcycles (17.7%), and manufacturing (12.0%).

Among the male workforce, 45.8 percent are employed in agriculture forestry and fishing,

13.2 percent in transport and storage, 8.9 percent in manufacturing, and equal proportions in

construction (7.8%) and transport and storage (7.8%), while 7.4 percent are employed in

retail; repair of motor vehicles and motorcycles. Among the females, 30.0 percent are

employed in agriculture forestry and fishing, a little over one quarter (26.4 percent) in

wholesale and retail; repair of motor vehicles and motorcycles and 10.1 percent in

accommodation and food service activities.

4.5 Employment Status

Employment status refers to the status of a person in an establishment where he/she works or

previously worked. Table 4.4 shows employed population 15 years and older by employment

status and sex in Akwapem North Municipality as recorded at the 2010 Population and

Housing Census. The majority (72.3%) of the economically active population are self-

employed with or without employees, 17.2 percent are employee, and 6.0 percent were

contributing family workers. Less than one percent are domestic workers (0.5%) or are in

apprenticeship training (0.1%).

For the sexes, a larger proportion of males (23.5%) compared to females (11.8%) are

employees, while a higher percentage of the females (77.5%) than the males (66.0%) are self-

employed with or without employees. In addition, more females (6.9%) than males (5.0%)

are contributing family workers.

Table 4.4: Employed population 15 years and older by employment status and sex

Employment Status

Both sexes

Male

Female

Number Percent

Number Percent

Number Percent

Total 52,480 100.0

24,053 100.0

28,427 100.0

Employee 9,001 17.2

5,653 23.5

3,348 11.8

Self-employed without employee(s) 35,399 67.5

14,468 60.2

20,931 73.6

Self-employed with employee(s) 2,514 4.8

1,396 5.8

1,118 3.9

Casual worker 839 1.6

578 2.4

261 0.9

Contributing family worker 3,166 6.0

1,198 5.0

1,968 6.9

Apprentice 1,242 2.4

610 2.5

632 2.2

Domestic employee (House help) 239 0.5

110 0.5

129 0.5

Other 80 0.2 40 0.2 40 0.1 Source: Ghana Statistical Service, 2010 Population and Housing Census

4.6 Employment Sector

Employment sector refers to the sector in which a person worked or is working. It covers

public, private informal, private formal, semi-public/parastatal, non-governmental

organizations (NGOs), and international organizations.

As shown in Table 4.5, higher proportions of both male and females are employed in the

private informal sector but the proportion is slightly higher for females than males (89.8%

versus 81.9%), whereas more males than females are employed in the public (government)

sector (11.0% versus 7.3%) and the private informal sector (6.7% versus 2.6%).

39

Table 4.5: Employed population 15 years and older by employment sector and sex

Employment sector

Both sexes

Male

Female

Number Percent Number Percent Number Percent

Total 52,480 100.0

24,053 100.0

28,427 100.0

Public (Government) 4,728 9.0

2,652 11.0

2,076 7.3

Private Formal 2,357 4.5

1,618 6.7

739 2.6

Private Informal 45,184 86.1

19,653 81.7

25,531 89.8

Semi-Public/Parastatal 42 0.1

33 0.1

9 0.0

NGOs (Local and International) 147 0.3

77 0.3

70 0.2

Other International Organizations 22 0.0 20 0.1 2 0.0

Source: Ghana Statistical Service, 2010 Population and Housing Census

Figure 4.1 shows the distribution of the population aged 15 years and older by employment

sector. As can be seen from Figure 4.1, reveals that the private informal sector is the largest

employer in the Akwapem North Municipality, accounting for a large majority (86.1%) of the

employed population. The other important sector is the public (government) sector which

employed 9.0 percent of the workers. Only 4.5 percent of the employed population works in

the private formal sector.

Figure 4.1: Employed population 15 years and older by employment sector

9% 5%

86%

0% 0%

0%

Employment Sector

Public (Government) Private Formal Private Informal

Semi-Public/Parastatal NGOs (Local and International) Other International Organisations

Source: Ghana Statistical Service, 2010 Population and Housing Census

40

CHAPTER FIVE

INFORMATION COMMUNICATION TECHNOLOGY

5.1 Introduction

Information Communication Technology (ICT) has become an important tool in today’s

knowledge-based information society and economy. The role of ICT in an emerging

economy such as Ghana’s, has been widely recognized at various levels. The recognition is

reflected in actions such as the development and deployment of a national ICT infrastructure,

institutional and regulatory framework for managing the sector, promoting the use of ICT in

all sectors of the economy, implementing e-governance in all government institutions and the

construction of a National Data Centre as well as Regional Innovation Centres (PHC,2010).

This chapter examines access of individuals to mobile phones and the Internet and access of

households to desktop/laptop computers and fixed telephone line.

5.2 Ownership of mobile phones

Table 5.1 shows the population 12 years and older who have mobile phone by sex. A total of

45,765 persons in the municipality have mobile phones, representing 47.7 percent of total

population 12 years and older as can be seen from the Table 5.1. Out of this total, slightly

more than half (50.1%) are females. Overall, mobile phone ownership in the Municipality can

be described as relatively extensive.

5.3 Use of Internet Facilities

Table 5.1 further indicates that out of 5,791 persons aged 12 years and older, representing

only 4.6 percent of the total population reported using the internet in the municipality. There

are variations in the usage of internet facility by sex. About 68.0 percent of males used the

Internet compared to 32 percent of females. The results point to the wide digital gap between

the Municipality and the rest of the world.

Table 5.1: Population 12 years and older by mobile phone ownership,

internet facility usage, and sex

Sex

Population 12

years and older

Population having

mobile phone

Population using

internet facility

Number Percent Number Percent Number Percent

Total 96,015 100.0 45,765 100.0 5,791 100.0

Male 43,391 45.2

22,572 49.3

3,799 65.6

Female 52,624 54.8 23,193 50.7 1,992 34.4

Source: Ghana Statistical Service, 2010 Population and Housing Census

41

5.4 Household Ownership of Desktop or Laptop Computers

Information management tools such as desktop and laptop computers facilitate the

accessibility and processing of information, including the use of the internet, electronic mail

and other services. Household ownership of desktop/laptop computer is shown in Table 5.2.

As can be seen from Table 5.2, 1,943 household heads out of the total of 33,322 reported

having desktop/laptop computer. This constituted only 5.8 percent of all households. Again,

the rate of ownership is higher in male-headed households than female-headed households

(71.6% against 28.4%).

Table 5.2: Households having desktop/laptop

computers by sex of head

Sex

Total

Households

having desktop/

laptop computers

Number Percent

Number Percent

Total 33,322 100.0 1,934 100.0

Male 18,381 55.2 1,384 71.6

Female 14,941 44.8 550 28.4 Source: Ghana Statistical Service, 2010 Population and Housing Census

42

CHAPTER SIX

DISABILITY

6.1 Introduction

Persons with disabilities (PWD) have been defined as those who are unable to or are

restricted in the performance of specific tasks/activities due to loss of function of some part

of the body as a result of impairment or malformation. Generally, persons with disability

(PWDs) face stigmatization and discrimination, especially in traditional societies in Ghana.

There are few institutions that meet the needs of the PWDs, such as School for the Blind in

Akwapem Akropong in Eastern region and School for the Deaf in Cape Coast in the Central

region.

For the first time, the 2010 Population Census collected data on disability in the country. This

chapter examines disability in the district with respect to the number, sex, locality of

residence and economic activity status of PWDs.

6.2 Population with Disability

Table 6.1 shows that there are 4,097 persons with some form of disability in the Municipality,

representing 3.0 percent of the total population. The data also shows that there are more

persons with disability in the female population (3.2%) compared to the male population

(2.7%). The data show that visual impairment (29.0%) is the most common type of disability

among the PWDs, followed closely by physical challenges (27.0%) and hearing impairment

(10.0%). Other PWDs suffer from intellectual challenges (9.0%), emotional challenges

(9.0%) and speech impairment (8.0%). Generally, there is no significant difference in the

types of disabilities is observed between the sexes. In speech disability, males recorded 3.8

percent higher than females and emotion and intellect deficiencies recorded proportional

differences to the advantage of females (1.2%) and males (1.0%).

The distribution of PWDs by locality of residence shows that disability varies in the rural and

urban populations of the district. The percentage of PWDs in the urban population (2.8%)

was slightly higher than the corresponding share in the rural population (2.7%). Concerning

type of disability, visual impairment and physical challenges were the commonest types

among PWDs in both urban (38.1% and 33.7% respectively) and rural areas (36.7% and

33.5% respectively). However, a higher proportion of PWDs in the urban areas reported

intellectual and emotional challenges (13.7% and 14.5% respectively) than their rural

counterparts (10.9% and 10.0% respectively).

43

Table 6.1: Population by type of locality, disability type and sex

Disability Type

Both sexes

Male Female

Number Percent

Number Percent Number Percent

All localities

Total 136,483 100.0

64,028 100.0

72,455 100.0

Without disability 132,386 97.0

62,283 97.3

70,103 96.8

With disability 4,097 3.0

1,745 2.7

2,352 3.2

Sight 1,550 29.0

648 37.1

902 38.4

Hearing 551 10.0

243 13.9

308 13.1

Speech 447 8.0

229 13.1

218 9.3

Physical 1,419 27.0

586 33.6

833 35.4

Intellect 460 9.0

206 11.8

254 10.8

Emotion 499 9.0

201 11.5

298 12.7

Other 416 8.0

196 11.2

220 9.4

Urban

Total 46,562 100.0

20,877 100.0

25,685 100.0

Without disability 45,175 97.0

20,299 97.2

24,876 96.9

With disability 1,387 3.0

578 2.8

809 3.1

Sight 521 37.6

220 38.1

301 37.2

Hearing 193 13.9

79 13.7

114 14.1

Speech 152 11.0

78 13.5

74 9.1

Physical 479 34.5

195 33.7

284 35.1

Intellect 181 13.0

79 13.7

102 12.6

Emotion 215 15.5

84 14.5

131 16.2

Other 65 4.7

29 5.0

36 4.4

Rural

Total 89,921 100.0

43,151 100.0

46,770 100.0

Without disability 87,211 97.0

41,984 97.3

45,227 96.7

With disability 2,710 3.0

1,167 2.7

1,543 3.3

Sight 1,029 38.0

428 36.7

601 39.0

Hearing 358 13.2

164 14.1

194 12.6

Speech 295 10.9

151 12.9

144 9.3

Physical 940 34.7

391 33.5

549 35.6

Intellect 279 10.3

127 10.9

152 9.9

Emotion 284 10.5

117 10.0

167 10.8

Other 351 13.0 167 14.3 184 11.9

Source: Ghana Statistical Service, 2010 Population and Housing Census

6.3 Disability and economic activity status

Table 6.2 presents the economic activity status of persons 15 years and older with disability

by activity status and sex. The majority (59.4%) of PWDs are not economically active as

indicated in the table. Of the economically active disable population, the majority (38.2%) are

employed. A similar distributional pattern of economic activity status of PWDs can be

observed for the sexes.

44

Table 6.2: Persons 15 years and older with disability by economic activity status

and sex

Sex/Disability type

All status

Employed Unemployed

Economically not

active

Number Percent Number Percent Number Percent Number Percent

Both Sexes

Total 86,454 100.0

52,480 60.7

4,628 5.4

29,346 33.9

No disability 82,719 100.0

51,052 61.7

4,538 5.5

27,129 32.8

With a disability 3,735 100.0

1,428 38.2

90 2.4

2,217 59.4

Sight 1,448 100.0

584 40.3

28 1.9

836 57.7

Hearing 491 100.0

184 37.5

17 3.5

290 59.1

Speech 358 100.0

124 34.6

11 3.1

223 62.3

Physical 1,332 100.0

380 28.5

26 2.0

926 69.5

Intellectual 403 100.0

88 21.8

19 4.7

296 73.4

Emotional 467 100.0

166 35.5

12 2.6

289 61.9

Other 370 100.0

204 55.1

19 5.1

147 39.7

Male

Total 38,560 100.0

24,053 62.4

2,055 5.3

12,452 32.3

No disability 37,021 100.0

23,373 63.1

2,020 5.5

11,628 31.4

With a disability 1,539 100.0

680 44.2

35 2.3

824 53.5

Sight 589 100.0

273 46.3

9 1.5

307 52.1

Hearing 215 100.0

101 47.0

7 3.3

107 49.8

Speech 179 100.0

76 42.5

6 3.4

97 54.2

Physical 537 100.0

172 32.0

14 2.6

351 65.4

Intellectual 176 100.0

40 22.7

10 5.7

126 71.6

Emotional 179 100.0

72 40.2

5 2.8

102 57.0

Other 171 100.0

95 55.6

7 4.1

69 40.4

Female

Total 47,894 100.0

28,427 59.4

2,573 5.4

16,894 35.3

No disability 45,698 100.0

27,679 60.6

2,518 5.5

15,501 33.9

With a disability 2,196 100.0

748 34.1

55 2.5

1,393 63.4

Sight 859 100.0

311 36.2

19 2.2

529 61.6

Hearing 276 100.0

83 30.1

10 3.6

183 66.3

Speech 179 100.0

48 26.8

5 2.8

126 70.4

Physical 795 100.0

208 26.2

12 1.5

575 72.3

Intellectual 227 100.0

48 21.1

9 4.0

170 74.9

Emotional 288 100.0

94 32.6

7 2.4

187 64.9

Other 199 100.0 109 54.8 12 6.0 78 39.2 Source: Ghana Statistical Service, 2010 Population and Housing Census

6.4 Disability and Education

Table 6.3 presents the distribution of the population of PWDs 3 years and older by level of

education. Out of the 4,055 persons with disability in the municipality, 38.5 percent had

never been school, 49.0 percent had attended basic education, and 9.7 percent had attained

secondary/SSS/SHS and higher levels of education.

45

Table 6.3: Population 3 years and older by sex, disability type and level of education

Sex/Type Total

Never

attended

Nursery

Kinder-

garten

Primary

JSS/

JHS

Middle

SSS/

SHS

Secon-

dary

Voc/

Tec/

Comm

Post

middle/

secondary

certificate

Post-

secondary

diploma

Bachelor ‘s

degree

Post graduate

(Cert. Diploma

Masters PHD

etc.)

Total 126,248 18877 2,502 7,914 34,465 24,724 17,556 9,145 2,397 2,243 1,674 2,489 1,854 408

No disability 122,193 17314 2,485 7,859 33,793 24,431 16,588 9,053 2,289 2,148 1,599 2,430 1,806 398

With a disability 4,055 1563 17 55 672 293 968 92 108 95 75 59 48 10

Sight 1,536 586 9 16 234 106 377 45 41 33 30 29 26 4

Hearing 543 259 - 13 84 34 111 9 10 10 5 3 3 2

Speech 439 219 6 14 68 26 69 10 9 6 8 2 2 -

Physical 1,414 615 5 12 197 70 363 15 35 35 33 20 14 -

Intellectual 453 224 2 7 76 22 87 6 20 5 2 2 - -

Emotional 494 219 3 1 70 44 118 11 10 8 5 4 1 -

Other 410 128 - 8 90 42 88 11 9 16 7 2 5 4

Male

Total 58,814 5934 1,249 4,159 15,922 12,009 8,307 4,927 1,477 1,022 797 1,528 1,206 277

No disability 57,087 5497 1,234 4,121 15,633 11,858 7,833 4,875 1,397 963 745 1,492 1,168 271

With a disability 1,727 437 15 38 289 151 474 52 80 59 52 36 38 6

Sight 644 165 7 12 90 49 181 27 31 27 16 15 22 2

Hearing 241 91 - 6 48 20 52 7 9 3 2 1 2 -

Speech 225 89 4 5 49 17 34 7 9 2 5 2 2 -

Physical 582 160 5 9 66 38 206 6 21 21 24 16 10 -

Intellectual 202 79 2 6 43 11 37 4 16 3 - 1 - -

Emotional 197 72 3 1 22 23 56 5 9 1 4 - 1 -

Other 192 39 - 8 43 20 46 5 7 11 4 2 3 4

Female

Total 67,434 12943 1,253 3,755 18,543 12,715 9,249 4,218 920 1,221 877 961 648 131

No disability 65,106 11817 1,251 3,738 18,160 12,573 8,755 4,178 892 1,185 854 938 638 127

With a disability 2,328 1126 2 17 383 142 494 40 28 36 23 23 10 4

Sight 892 421 2 4 144 57 196 18 10 6 14 14 4 2

Hearing 302 168 - 7 36 14 59 2 1 7 3 2 1 2

Speech 214 130 2 9 19 9 35 3 - 4 3 - - -

Physical 832 455 - 3 131 32 157 9 14 14 9 4 4 -

Intellectual 251 145 - 1 33 11 50 2 4 2 2 1 - -

Emotional 297 147 - - 48 21 62 6 1 7 1 4 - -

Other 218 89 - - 47 22 42 6 2 5 3 - 2 -

46

CHAPTER SEVEN

AGRICULTURAL ACTIVITIES

7.1 Introduction

Agriculture is a crucial sector for reducing poverty and achieving the Millennium

Development Goals (MDGS) in Ghana because agriculture has been the mainstay of the

economy. But the Food and Agricultural Organization (FAO) has noted that despite the

unquestionable importance of agriculture, it is arguably the least known sector of many

economies in terms of hard facts and statistics (FAO 2012). Any strategy geared towards

reducing poverty and food insecurity must be based on timely and accurate information that

can help to measure the impact of agricultural policies and programmes. The census data

analyzed and discussed in this chapter on agricultural activity include households who are

engaged in agricultural activities by locality (rural/urban), types of crops cultivated crop

farming, tree planting, rearing of livestock and breeding of fish for sale or for family

consumption.

This chapter examines agricultural activities of households in the municipality in terms of the

size and distribution of agricultural household and types of farming activities.

7.2 Households in Agriculture

An agricultural household is one that engages generally in agricultural activities or is said to

be agricultural household if one of its members engages in agricultural production even if not

earning from agricultural activities alone.

7.2.1 Household size and distribution (urban/rural)

Table 7.1 is on the distribution of households by agricultural activities in the municipality.

The municipality recorded a total of 15,703 agricultural households, representing 47.1

percent of all households. With regard to locality of residence, there are more agricultural

households in the rural areas (58.6%) than in urban areas (27.1%).

Table 7.1: Size of households by agricultural activities

Type of activity Number Percent Urban Rural

Total Households 33,322 100.0

100.0

100.0

Households engages in Agriculture 15,703 47.1

27.1

58.6

Crop Farming 14,686 93.5

88.4

94.9

Tree Planting 260 1.7

2.5

1.4

Livestock Rearing 5,434 34.6

26.0

36.9

Fish Farming 13 0.1 0.2 0.0

Source: Ghana Statistical Service, 2010 Population and Housing Census

7.2.2 Types of agricultural activities

As shown in Table 7.1 and Figure 7.1 four types of agricultural activities are identified in the

municipality, namely crop farming, livestock rearing, fish farming and tree planting. The

major agricultural activity in the municipality is crop farming (93.5%), followed by livestock

rearing (34.6%) and tree planting (1.7%). Less than one percent of agricultural households

47

are engaged in fish farming (0.1%). As expected, a higher proportion of rural agricultural

households are engaged in crop farming compared to urban agricultural households (94.9%

vs. 88.4%). Livestock rearing is also relatively more common in the rural areas (36.9%) than

urban areas (26.0%).

Figure 7.1: Percentage size of households in agriculture activities

Source: Ghana Statistical Service, 2010 Population and Housing Census

7.3 Distribution of livestock, animals reared and keepers

As noted earlier, livestock rearing is the second most important agricultural activity in the

municipality. Table 7.2 shows the distribution of livestock and keepers. The 2010 Population

and Housing Census counted a total of 156,123 livestock with 8,722 keepers. The highest

proportion of livestock reared in the municipality is chicken (61.9%) with an average of 25

birds per keeper , followed by goat (14.8%) with an average of 8 animals per keeper, pig

(7.7%) with an average of 29 animals per keeper, and sheep (6.0%) with average of 8

animals keepers per.. Fish farming (2.3%), turkey rearing (1.5%) and duck rearing (1.5%)

are other livestock activities undertaken by agricultural households on smaller scales in the

municipality.

48

Table 7.2: Distribution of livestock, other animals and keepers

Type of livestock

Number of

Animals

Number of

keepers

Average

Animal per

Keeper

All livestock 156,123 8,722 18

Beehives 114 11 10

Cattle 1,571 66 24

Chicken 96,617 3,819 25

Dove 748 40 19

Duck 2,441 199 12

Goat 23,080 2,817 8

Grass-cutter 706 42 17

Guinea fowl 551 30 18

Ostrich 954 30 32

Pig 12,054 413 29

Rabbit 1,387 47 30

Sheep 9,327 1,111 8

Silk worm 182 14 13

Snail 200 7 29

Turkey 2,287 48 48

Other 117 21 6

Fish farming 3,587 6 598

Inland fishing 200 1 200

Marine fishing 0 0 0

Other 117 21 6

Marine fishing 0 0 0

49

CHAPTER EIGHT

HOUSING CONDITIONS

8.1 Introduction

In many developing countries, where population increase has been rapid while economic

growth has been slow or stagnant, there have been deficits in the supply of facilities such as

housing. The situation in urban centres especially has been worsened due to rapid rates of

urbanization. Concerns for human wellbeing conditions associated with housing led to the

introduction of housing into the 2000 Round of Population and Housing Census. This chapter

presents the findings on housing and housing conditions that pertained in the municipality at

the 2010 Population and Housing Census. It examines the housing stock, type of dwelling,

room occupancy, holding and tenancy, lighting and cooking facilities, bathing and toilet

facilities, waste disposal and source of water for domestic use among others.

8.2 Housing stock

The distribution of housing stock and population in dwelling units are discussed in this

section. The total stock of houses in the municipality is 22,896 of which the highest

proportion is located in the rural areas (69.0%) compared with urban areas (31.0%). The

average population per house in the municipality is 5.9 persons per house and is almost equal

to the regional average of 6.0 persons per house. The average number of households per

house is 1.5. There are 6.4 persons per house in the urban areas compared to 5.6 persons per

house in the rural areas.

Table 8.1: Stock of houses and households

Categories

Total

country Region District Urban Rural

Total population 24,658,823 2,633,154 136,483 46,562 89,921

Total household population 24,076,327 2,574,549 134,359 45,691 88,668

Number of houses 3,392,745 431,697 22,896 7,088 15,808

Number of households 5,467,054 632,045 33,322 12,146 21,176

Population per house* 7.1 6.0 5.9 6.4 5.6 Source: Ghana Statistical Service, 2010 Population and Housing Census

8.3 Type of dwelling units

From Table 8.2, the Akwapem Municipality has a total of 33,322 dwelling units. The highest

proportion of dwelling units are found in compound houses (52.1%), followed by separate

houses (31.4%). Flats/apartments are not common (4.2%) in the Municipality. Tents (0.2%),

kiosks and containers (0.4%) as dwelling units is insignificant in the Municipality.

Compound houses (58.7%) are more predominant (58.7%) in urban localities than rural

(48.3%). The reverse is the case for separate houses (36.4% for rural and 22.6% for urban).

For the sexes, a higher proportion of female-headed households (58.0%) reside in compound

houses compared to male-headed households (47.0%).On the contrary, male-headed

households compared to female-headed households have a higher percentage in separate

houses (34.7% against 27.3%) and semi-detached houses (7.5% against 7.0%).

50

Table 8.2: Type of dwelling by sex of household head and type of locality

Type of dwelling

District

Total

Total Male

headed

Female

headed Urban Rural Country Region Number Percent

Total 5,467,054 632,045

33,322 100.0 100.0 100.0 100.0 100.0

Separate house 1,471,391 193,719

10,459 31.4 34.7 27.3 22.6 36.4

Semi-detached house 391,548 42,458

2,418 7.3 7.5 7.0 9.3 6.1

Flat/Apartment 256,355 16,839

1,396 4.2 4.4 3.9 6.4 2.9

Compound house (rooms) 2,942,147 349,682

17,359 52.1 47.3 57.9 58.7 48.3

Huts/Buildings (same

compound) 170,957 17,381

792 2.4 2.9 1.8 0.5 3.5

Huts/Buildings (different

compound) 36,410 3,236

339 1.0 1.2 0.7 0.1 1.5

Tent 10,343 950

55 0.2 0.2 0.2 0.3 0.1

Improvised home

(kiosk/container etc.) 90,934 3,055

132 0.4 0.5 0.3 0.6 0.3

Living quarters attached to

office/shop 20,499 1,736

97 0.3 0.3 0.3 0.4 0.2

Uncompleted building 66,624 2,335

248 0.7 0.9 0.6 1.0 0.6

Other 9,846 654 27 0.1 0.1 0.1 0.1 0.1

Source: Ghana Statistical Service, 2010 Population and Housing Census

8.4 Main material for outer walls

As shown in Table 8.4, cement blocks/concrete and mud brick/earth are the two main

construction materials used by households for outer walls of dwellings in the Municipality.

Of the two construction materials, cement blocks/concrete accounted for 63.6 per cent of all

types of materials used for wall construction, followed by mud brick/earth (28.7%). The use

of cement blocks/concrete for outer wall construction is more common in the urban than rural

localities (85.6% versus 51.3%) while a substantially higher proportion of the outer wall of

dwelling units in the rural areas are constructed with brick/earth (40.6%) compared to urban

areas (7.5%). Outer walls made of thatch/palm leaf sandcrete/landcrete and wood are not

common in both urban and rural areas.

Table 8.3: Main construction material for outer wall

Material for Outer wall

District

Total Total

Urban Rural Country Region Number Percent

Total 5,817,607 686,478 34,887 100.0 100.0 100.0

Mud brick/earth 1,991,540 266,725 10,017 28.7 7.5 40.6

Wood 200,594 12,028 417 1.2 1.0 1.3

Metal sheet/slate/asbestos 43,708 4,268 414 1.2 1.7 0.9

Stone 11,330 1,182 285 0.8 1.8 0.2

Burnt bricks 38,237 6,481 229 0.7 0.4 0.8

Cement blocks/concrete 3,342,462 370,691 22,190 63.6 85.6 51.3

Landcrete 104,270 19,885 334 1.0 0.1 1.4

Bamboo 8,206 954 54 0.2 0.1 0.2

Palm leaf/thatch (grass)/raffia 38,054 1,202 153 0.4 0.1 0.6

Other 39,206 3,062 794 2.3 1.5 2.7 Source: Ghana Statistical Service, 2010 Population and Housing Census

51

8.5 Main materials for floors

As shown in Table 8.4, cement is the main material used for floors of dwelling units,

accounting for 78.4 percent of the floors of all dwelling units. The second commonest

material used for floors is earth/mud which accounted for 17.2 percent of all floors. The other

types of materials used for floors include ceramic, porcelain, granite and marble tiles (1.0%).

Cement is the most common material used for floors in both rural and urban areas, with the

proportion higher in the urban areas (84.4%) than rural areas (74.9%). However, a little more

than one in five (21.7%) of floors of dwelling units in the rural areas are constructed with

earth/mud compared to about one in ten (9.3%) in the urban areas.

Table 8.4: Main construction materials for the floor

Materials for the floor

District

Total

Total

Urban Rural Country Region Number Percent

Total 5,467,054 632,045

33,322 100.0 100.0 100.0

Earth/mud 872,161 118,931

5,722 17.2 9.3 21.7

Cement/concrete 4,255,611 491,586

26,111 78.4 84.4 74.9

Stone 32,817 6,076

354 1.1 1.3 0.9

Burnt brick 6,537 710

21 0.1 0.1 0.1

Wood 52,856 1,345

58 0.2 0.2 0.1

Vinyl tiles 57,032 2,914

319 1.0 1.6 0.6

Ceramic/porcelain/granite/marble tiles 88,500 5,404

330 1.0 1.4 0.8

Terrazzo/terrazzo tiles 85,973 3,833

333 1.0 1.3 0.8

Other 15,567 1,246 74 0.2 0.4 0.1 Source: Ghana Statistical Service, 2010 Population and Housing Census

8.6 Main roofing materials

Type of material used for roofing is one of the key indicators of quality of housing and as such an

indicator of welfare of occupants of dwellings. As can be seen from Table 8.5, almost all the

occupied dwelling units in the municipality are roofed with metal sheets (91.2%). Only a very

small proportion of dwelling units are roofed with slate/asbestos (3.0%), thatch/ palm leaf/ raffia

(1.7%) and cement/concrete (1.6%).

Table 8.5: Main construction material for roofing

Main Roofing material

District

Total

Total

Urban Rural Country Region Number Percent

Total 5,817,607 686,478

34,887 100.0 100.0 100.0

Mud/mud bricks/earth 80,644 3,693

149 0.4 0.2 0.6

Wood 45,547 3,527

175 0.5 0.5 0.5

Metal sheet 4,152,259 604,209

31,817 91.2 89.8 92.0

Slate/asbestos 759,039 8,831

1,052 3.0 5.6 1.5

Cement/concrete 141,072 5,561

569 1.6 2.5 1.1

Roofing tile 31,456 1,012

83 0.2 0.4 0.2

Bamboo 71,049 4,630

249 0.7 0.1 1.1

Thatch/palm leaf or raffia 500,606 52,372

601 1.7 0.4 2.5

Other 35,935 2,643 192 0.6 0.5 0.6

Source: Ghana Statistical Service, 2010 Population and Housing Census

52

There are urban-rural differentials regarding roofing materials used. Metal sheet is the most

widely used roofing material in both places of residence though a higher percentage of

dwellings in urban localities (94.1%) than those in rural areas (83%) had metal sheet roofs.

Thatch/palm leaf/raffia is used as roofing material for a far larger percentage of dwellings in

rural areas (12.7%) than in the urban areas (1.4%).

8.7 Room occupancy

Table 8.5 provides information on household size and number of sleeping rooms in occupied

dwellings. More than half (53.4%) of all the occupied dwellings in the district has one

sleeping room. Two sleeping rooms (26.6%) in occupied dwellings is the next highest,

followed by occupied dwelling units with three sleeping rooms (10.8%). Those with four or

more rooms constitute close to one-tenth (9.1%). High proportions (ranging between 50 to 90

percent) of households with sizes between 1-4 members occupied one room; and about 60

percent and more of the households with 5 to 7 members occupied one or two rooms. Overall,

the room occupancy rates suggest that there is overcrowding of sleeping arrangements among

the households in the municipality.

Table 8.6: Household size and number of sleeping rooms occupied in dwelling unit

House-

hold

size

Number of sleeping rooms

Total One

room

Two

rooms

Three

rooms

Four

rooms

Five

rooms

Six

rooms

Seven

rooms

Eight

rooms

Nine

rooms

or more Number Percent

Total 33,322 100.0

53.4 26.6 10.8 5.0 2.0 1.1 0.4 0.3 0.4

1 6,800 100.0

90.4 7.4 1.1 0.5 0.2 0.1 * 0.1 0.1

2 4,696 100.0

69.6 25.6 2.6 1.1 0.5 0.2 0.1 0.1 0.1

3 4,668 100.0

59.3 28.8 8.7 1.7 0.6 0.3 0.3 * 0.3

4 4,663 100.0

49.6 32.5 11.3 4.7 1.0 0.5 0.1 * 0.1

5 4,070 100.0

38.0 37.8 15.2 6.0 1.6 0.8 0.2 0.1 0.2

6 3,016 100.0

31.1 36.8 17.9 7.6 3.6 2.0 0.2 0.5 0.2

7 1,932 100.0

21.3 37.8 22.5 11.2 3.0 2.0 0.9 0.5 0.8

8 1,283 100.0

16.0 33.2 26.0 13.2 5.3 3.5 1.5 0.6 0.8

9 810 100.0

10.0 28.5 27.8 17.5 9.1 3.2 2.0 0.7 1.1

10+ 1,384 100.0 7.9 17.8 22.1 19.9 13.2 7.2 4.0 3.5 4.3

Source: Ghana Statistical Service, 2010 Population and Housing Census

8.8 Access to utilities and household facilities

8.8.1 Main source of lighting

As shown in Table 8.7, there are three main sources of lighting for households in the

municipality. These are electricity (mains) [60.5%], kerosene lamp (29.1%) and

flashlight/torch (8.5%). The proportion of households using electric generators as the main

source of lighting is under one percent (0.6%). The use of private generators is a recent

phenomenon which was brought about by the frequent power outages and load shedding.

There are rural-urban variations in the sources of lighting. Over 80 percent (82.4%) of

households in urban areas obtain light from electricity (mains), compared with less than half

(47.9 percent) in rural areas (Table 8.7). Almost two-fifths (38.2%) of households in rural

areas use kerosene lamp as main source of light against 13.2 percent in urban areas. Again,

53

11.8 percent of rural households use flashlight as main source of light compared with only

2.7 percent in urban areas.

Overall, the analysis shows that kerosene lamp which used to be the most common source of

light for majority of households in the municipality decades ago is no longer the dominant

source. Although flashlight has always been used in Ghana, it is gradually becoming a major

source of light with the introduction of several long-lasting batteries and other rechargeable

varieties Although there are efforts to introduce non-conventional energy sources in the

country, only a very small proportion of households in the municipality, as elsewhere in

Ghana, use these sources. The proportion of households depending on solar energy as their

main source of lighting is very low (0.2%).

Table 8.7: Main source of lighting

Main source of light

District

Total

Total

Urban Rural Country Region Number Percent

Total 5,467,054 632,045

33,322 100.0 100.0 100.0

Electricity (mains) 3,511,065 369,961

20,152 60.5 82.4 47.9

Electricity (private generator) 36,142 4,282

203 0.6 0.5 0.7

Kerosene lamp 971,807 159,439

9,693 29.1 13.2 38.2

Gas lamp 9,378 1,135

58 0.2 0.1 0.2

Solar energy 9,194 1,018

50 0.2 0.2 0.1

Candle 41,214 2,595

160 0.5 0.6 0.4

Flashlight/torch 858,651 90,643

2,824 8.5 2.7 11.8

Firewood 13,241 1,593

109 0.3 0.1 0.4

Crop residue 4,623 447

46 0.1 0.1 0.2

Other 11,739 932 27 0.1 0.1 0.1 Source: Ghana Statistical Service, 2010 Population and Housing Census

8.8.2 Main source cooking fuel

As shown in Table 8.8 the three main sources of energy for cooking by households in the

Municipality is firewood (40.8%) and charcoal (39.0%), accounting for more three quarters

of households. The use of gas (16.1%) as cooking fuel is relatively low in the Municipality

which may be due to the difficulty of obtaining gas. The proportion of dwelling units where

no cooking is done is 2.8 percent which may be a reflection on the increase in single-member

households. In rural areas, because wood is locally available, it is the main source of cooking

fuel for 54.5 percent of households compared with 16.9 percent in urban areas. In contrast,

charcoal, which is often produced for urban dwellers, was used by 51.4 percent of households

in urban areas compared to 31.9 percent in rural areas. The use of wood and charcoal as the

main sources of fuel by households has implications for the control of deforestation in the

Municipality.

8.8.3 Cooking space used by household

The distribution of cooking space is presented in Table 8.8. Slightly more than one third

(34.7%) of households have separate rooms in their dwelling units for the exclusive use by

the household. Close to one quarter (23.8%) of households cook on verandas. Slightly more

than 13 percent (13.3%) percent used open space in compound and 12.3 percent used a

structure with roof but without walls. A little over four percent (4.2%) have no cooking

space.

54

Table 8.8: Main source of cooking fuel, and cooking space used by households

Source of cooking fuel/cooking space

District

Total

Total

Urban Rural Country Region

Number Percent

Main source of cooking fuel for household

Total 5,467,054 632,045

33,322 100.0 100.0 100.0

None no cooking 306,118 29,214

937 2.8 3.8 2.2

Wood 2,197,083 315,386

13,587 40.8 16.9 54.5

Gas 996,518 74,339

5,367 16.1 26.4 10.2

Electricity 29,794 3,438

133 0.4 0.4 0.4

Kerosene 29,868 3,393

176 0.5 0.7 0.4

Charcoal 1,844,290 203,053

13,009 39.0 51.4 31.9

Crop residue 45,292 2,198

75 0.2 0.2 0.2

Saw dust 8,000 548

23 0.1 0.1 0.1

Animal waste 2,332 147

8 0.0 0.0 0.0

Other 7,759 329

7 0.0 0.0 0.0

Cooking space used by household

Total 5,467,054 632,045

33,322 100.0 100.0 100.0

No cooking space 386,883 38,872

1,415 4.2 5.3 3.7

Separate room for exclusive use of household 1,817,018 230,426

11,571 34.7 37.8 32.9

Separate room shared with other household(s) 410,765 51,302

2,664 8.0 11.8 5.8

Enclosure without roof 117,614 10,220

520 1.6 1.0 1.9

Structure with roof but without walls 349,832 67,390

4,099 12.3 3.8 17.2

Bedroom/hall/living room) 74,525 7,798

524 1.6 1.6 1.6

Verandah 1,173,946 135,910

7,930 23.8 28.8 20.9

Open space in compound 1,115,464 87,662

4,432 13.3 9.7 15.4

Other 21,007 2,465 167 0.5 0.3 0.6

Source: Ghana Statistical Service, 2010 Population and Housing Census

There are urban-rural differences regarding the type of cooking space used by households. The

proportion of urban households (37.8%) with separate room for exclusive use by households

for cooking was higher compared to that of rural houses (32.9%). Similarly, the proportions

of households that used the veranda and open spaces in compound for cooking were lower in

the urban areas (23.8% and 13.3% respectively) than in rural areas (20.9% and 15.4%

respectively).

8.8.4 Main source of drinking water

The source of water supply, particularly for drinking has tremendous effect on the burden of

disease in a community. The main health benefit of clean water supply is a reduction in

diarrheal disease, although the effects on other diseases are also substantial. Water is often

classified as “improved” or “unimproved”. Sources considered as improved are household

connection to public pipe borne water supply system, public standpipe, bore-hole/pump/tube

well, protected (lined) dug well, protected spring, and rainwater collection. Unprotected wells

and springs, vendors, and tanker-trucks are considered unimproved.

The main source of drinking water for households in the municipality is presented in Table

8.9. Household drinking water in the municipality is obtained from six main sources: public

standpipe (4.1%), pipe-borne water outside the dwelling unit (21.1%), borehole or pump tube

well (26.1%), pipe-borne water inside the dwelling (12.2%), sachet water (10.2%), and rivers

and streams (14.1%).

55

Altogether, less than two-fifths (37.4%) of households in the Municipality obtained their

main source of water from pipe-borne source. This is above the regional average of 34.0

percent. It is important to note that about 10.2 percent of households in the Municipality

reported using sachet water as the main source of drinking water, a practice which was hardly

known in 2000. While the production of sachet water may provide jobs, the challenges posed

by sachet water are worth mentioning. Issues of unhygienic production and disposal of the

plastic are a nightmare in most big cities and towns in the country. Finally, about 16.1 percent

of dwelling units obtain water from unprotected wells, unprotected springs, rivers, streams,

ponds and lakes- sources considered as “unprotected‟.

There are differentials at the locality level in the main source of drinking water. In the urban

areas 64.5 percent of households obtained their drinking water from pipe borne sources

compared to 22.0 percent in rural areas. Consequently, the main source of drinking water in

the rural areas is bore-hole/pump/tube well (39.3%) and river/sprig (20.0%) against 3.1

percent and 3.9 percent respectively in the urban areas.

Table 8.9: Main source of water for drinking and other domestic purposes

Sources of water

District

Total

Total

Urban Rural Country Region Number Percent

Main source of drinking water for household

Total 5,467,054 632,045

33,322 100.0 100.0 100.0

Pipe-borne inside dwelling 790,493 51,123

4,060 12.2 23.7 5.6

Pipe-borne outside dwelling 1,039,667 91,863

7,040 21.1 34.8 13.3

Public tap/standpipe 712,375 71,616

1,378 4.1 6.0 3.1

Bore-hole/pump/tube well 1,267,688 177,097

8,689 26.1 3.1 39.3

Protected well 321,091 58,167

1,148 3.4 5.0 2.6

Rain water 39,438 7,948

1,403 4.2 5.3 3.6

Protected spring 19,345 2,570

311 0.9 2.0 0.3

Bottled water 20,261 1,232

91 0.3 0.3 0.2

Sachet water 490,283 53,638

3,410 10.2 12.2 9.1

Tanker supply/vendor provided 58,400 1,562

370 1.1 1.1 1.1

Unprotected well 112,567 9,712

514 1.5 1.6 1.5

Unprotected spring 12,222 1,751

80 0.2 0.3 0.2

River/stream 502,804 94,883

4,700 14.1 3.9 20.0

Dugout/pond/lake/dam/canal 76,448 8,624

115 0.3 0.7 0.1

Other 3,972 259

13 0.0 0.0 0.0

Main source of water for other domestic use of

household

Total 5,467,054 632,045

33,322 100.0 100.0 100.0

Pipe-borne inside dwelling 905,566 55,588

3,999 12.0 22.7 5.9

Pipe-borne outside dwelling 1,089,030 83,245

5,739 17.2 27.8 11.2

Public tap/standpipe 704,293 65,772

1,272 3.8 5.5 2.9

Bore-hole/pump/tube well 1,280,465 180,604

9,133 27.4 5.4 40.1

Protected well 465,775 95,179

1,956 5.9 7.2 5.1

Rain water 39,916 7,577

1,395 4.2 6.8 2.7

Protected spring 18,854 2,760

341 1.0 2.2 0.3

Tanker supply/vendor provided 100,048 1,975

427 1.3 1.4 1.2

Unprotected well 152,055 13,230

596 1.8 1.9 1.7

Unprotected spring 15,738 2,196

225 0.7 1.3 0.3

River/Stream 588,590 112,728

7,966 23.9 16.7 28.0

Dugout/pond/lake/dam/canal 96,422 9,850

192 0.6 1.0 0.3

Other 10,302 1,341 81 0.2 0.2 0.3

Source: Ghana Statistical Service, 2010 Population and Housing Census

56

8.8.5 Main source of water for other domestic use

As shown in Table 8.9, the main source of water for other domestic use in the municipality is

pip-borne water from all sources (37.2%), bore-hole/pump/tube well (27.4), and streams

(23.9%). Half of households in the municipality use bore-hole/ pump tube and rivers and

streams as their main source of water for domestic use. With respect to localities, the main

sources of water for other domestic use in the urban areas were pipe borne water from all

sources (56.0%) and river/stream (16.7%), while the main source in the rural areas is bore-

hole/pump/tube well (39.3%) and river/stream (20.0%).

8.9 Bathing and toilet facilities

8.9.1 Bathing facilities

Table 8.10 presents bathing facilities available in dwelling units in the municipality in 2010.

Bathing facilities used in the municipal are primarily of three main types: shared bathroom in

the same house (28.7%), bathroom for exclusive use (25.9%), and shared open bathing

cubicle (25.9%). The proportion of dwelling units that share an open cubicle as a bathroom

with others in the compound is only 5.4 percent.

There are differentials across localities in the types of bathing facilities used by households.

The proportion of dwelling units that had a bathroom for exclusive use is slightly higher in

urban (26.1%) than rural (25.2%) localities. More urban households than rural households

also reported using shared separate bathroom in the same house (39.6% and 22.5%

respectively). It is also noted that use of private open cubicle for bathing purposes is use by a

far larger proportion of rural households than urban households (14.8% as against 3.1%).

Table 8.10: Type of toilet facility and bathing facility used by household by

type of locality

Toilet facility/Bathing facility

District

Total

Total

Urban Rural Country Region Number Percent

Toilet facility used by household

Total 5,467,054 632,045

33,322 100.0 100.0 100.0

No facilities (bush/beach/field) 1,056,382 71,384

1,614 4.8 2.2 6.3

W.C. 839,611 55,161

4,271 12.8 22.1 7.5

Pit latrine 1,040,883 203,246

11,063 33.2 14.3 44.0

KVIP 572,824 100,193

5,874 17.6 21.7 15.3

Bucket/Pan 40,678 3,926

662 2.0 4.1 0.8

Public toilet (WC/KVIP/Pit Pan etc.) 1,893,291 195,950

9,745 29.2 35.2 25.8

Other 23,385 2,185

93 0.3 0.4 0.2

Bathing facility used by household

Total 5,467,054 632,045

33,322 100.0 100.0 100.0

Own bathroom for exclusive use 1,535,392 163,394

8,643 25.9 26.1 25.8

Shared separate bathroom in the same house 1,818,522 209,248

9,576 28.7 39.6 22.5

Private open cubicle 381,979 56,572

3,508 10.5 3.1 14.8

Shared open cubicle 1,000,257 131,234

8,646 25.9 25.0 26.5

Public bath house 140,501 2,817

216 0.6 1.1 0.4

Bathroom in another house 187,337 19,316

703 2.1 1.7 2.4

Open space around house 372,556 45,833

1,808 5.4 3.0 6.8

River/pond/lake/dam 14,234 1,996

26 0.1 0.1 0.1

Other 16,276 1,635 196 0.6 0.3 0.7

Source: Ghana Statistical Service, 2010 Population and Housing Census

57

8.9.2 Toilet facilities

The type of toilet facility available in a dwelling unit is an important indicator of the sanitary

condition of the unit as well as an indirect measure of the poverty status of a household.

Table 8.10 shows that five main types of toilet facilities are used in the municipality.

Arranged in order of availability for household members are the pit latrine (33.0%), public

toilet (29.2%), KVIP (17.6) and the water closet (12.8). About 5.0 percent of households

reported that they had no toilet facilities and therefore resorted to bush/beach/open fields.

Although the use of bucket/pan as toilet facility has been banned in Ghana so many years

ago, about 2.0 percent of households in the municipality continue to use the facility.

The proportion of households with no toilet facilities is slightly higher in rural (6.3%) than in

urban areas (2.2%). The proportions of urban households using the W.C (22.1%), public

toilet (35.2%) and KVIP (21.7%) is higher than rural households using same facilities

(7.5%, 25.8% and 15.3% respectively), while the use of pit latrines is higher in rural than

urban households (44.0% against 14.3%).

8.10 Method of Waste Disposal

8.10.1 Disposal of solid waste

One of the most intractable challenges of both urban and rural areas in Ghana, and in the

municipality in particular, is efficient solid waste (refuse) disposal. As shown in Table 8.11,

the most means of disposing solid waste (refuse) in the municipality is by public dump, either

dumping in a container or dumping unto open dump site (61.2%), followed by the burning of

solid waste by households (21.7%). Routine collection of waste from houses is used by a very

small percentage of households (3.4%). About another 6.0 percent of households dumped

their solid waste indiscriminately.

A higher percentage of rural households than urban households buried their solid waste (8.9%

versus 5.3%), while a higher proportion of urban households (6.2%) than the rural (1.7%)

have their solid waste collected by those who collect solid waste from house to house.

8.9.2 Disposal of liquid waste

In Table 8.11, the most common method of liquid waste disposal used by households is

throwing the waste onto the compound, followed by throwing onto the street or outside the

house (24.4%) and throwing in gutter (18.1%). A negligible proportion of households

disposed of their liquid waste through a sewerage system (0.2%). The throwing of liquid

waste into a gutter is more prevalent in the urban (33.7%) than rural areas (9.2%), while more

than half (56.5%) of rural households throw their waste onto the compound compared to a

quarter (25.3%) of urban households.

58

Table 8.11: Method of solid and liquid waste disposal by type of locality

Method of waste disposal

District

Total

Total

Urban Rural Country Region Number Percent

Solid waste

Total 5,467,054 632,045

33,322 100.0 100.0 100.0

Collected 785,889 26,049

1,123 3.4 6.2 1.7

Burned by household 584,820 102,501

7,179 21.5 10.9 27.6

Public dump (container) 1,299,654 143,820

3,955 11.9 13.7 10.8

Public dump (open space) 2,061,403 252,886

16,417 49.3 61.9 42.0

Dumped indiscriminately 498,868 63,321

1,949 5.8 1.5 8.4

Buried by household 182,615 37,144

2,529 7.6 5.3 8.9

Other 53,805 6,324

170 0.5 0.6 0.5

Liquid waste

Total 5,467,054 632,045

33,322 100.0 100.0 100.0

Through the sewerage system 183,169 8,228

679 2.0 4.3 0.8

Through drainage system into a gutter 594,404 33,511

1,885 5.7 9.7 3.4

Through drainage into a pit (soak away) 167,555 11,428

1,286 3.9 4.9 3.3

Thrown onto the street/outside 1,538,550 147,245

8,124 24.4 20.8 26.4

Thrown into gutter 1,020,096 106,945

6,044 18.1 33.7 9.2

Thrown onto compound 1,924,986 319,580

15,041 45.1 25.3 56.5

Other 38,294 5,108 263 0.8 1.3 0.5

Source: Ghana Statistical Service, 2010 Population and Housing Census

59

CHAPTER NINE

SUMMARY OF FINDINGS, CONCLUSIONS AND

POLICY IMPLICATIONS

9.1 Introduction

This chapter presents the key findings of the 2010 Population and Housing Census pertaining

to Akwapem North Municipality. Conclusions and policy implications resulting from the

findings are also discussed. As this is the first time of producing district level report, it is not

possible to establish trends in the population patterns and processes. It is therefore imperative

to continue this exercise in subsequent censuses

9.2 Summary of findings

Demographic characteristics

The report shows that the population size of the Akuapem North Municipality in 2010 is

136,483, representing 5.2 percent of the total population of Eastern region. Females (53.1%)

outnumber males resulting in a sex ratio of 88.4. The distribution of the population by

locality of residence indicates that the majority (63.9%) of the population reside in rural

localities. Total Fertility Rate (TFR) for the Municipality is 3.6 children per woman.

The age structure of the district’s population showed a relatively large proportion of children

less than 15 years (36.7%) and a significant proportion (12.9%) of older people (persons aged

60 years and older). The Municipality therefore has a high dependency ratio of 81.3

dependents (children under age 15 and persons 65 years and older) per 100 population in the

working age group (persons aged 15-64 years).

The Municipality has a total migrant population of 45,183, representing 33.3 percent of the

total population. The majority (58.3%) of the migrant population were born in other localities

in the Eastern region. Of the migrants born in other regions in Ghana, the majority (25.7%)

were born in the Volta region.

Social characteristics

The social characteristics covered included household characteristics, marital status,

nationality, religious affiliation, and literacy and education.

Household size, composition and structure

The municipality has a total of 33,322 households with a total of 134,359 household

members. The majority (68.1%) of these households were headed by males. The average

household size for the municipality is 4.0 persons which is slightly higher in female-headed

households than male-headed households (4.8 against 3.4).

Children constitute more than one third (38.0%) of household members in the Municipality.

About one fifth (24.8%) of all household members are household heads with almost tenth

(9.1%) being spouses. Grandchildren form 12.5 percent of all household members.

60

Single person households constitute the highest (20.4%) of the households in the

municipality. Slightly more than one in ten (10.5%) of households have eight members or

more, while households with nine members recorded the lowest proportion (2.4%). Although

the proportions of other relatives (7.0%), siblings (4.0%) and parent/parent-in-law (1.1%)

were relatively low, they are indications of the persistence of the traditional extended family

system in the municipality.

In respect of household structure, single person households constitute 5.1 percent of all

households. Households composed of head, his or her spouse and children constituted the

highest proportion of households (24.3%), followed by households with the single parent

extended structure (21.2%) and households made up of head, spouse, children and relative of

head (18.0%).

Marital status

Regarding marital status, 40.9 percent of the population had never married, 38.8 percent were

married and 4.0 percent were in informal/consensual union. In addition 16.3 percent had once

been married but were separated (3.3%), divorced (5.1% or widowed (7.9%). Just about equal

proportions of males and females were married (39.2% versus 38.4%). However, a higher

percentage of females than males were separated (5.4% versus 2.4%) and divorced (6.8%

versus 3%). Activity status of persons provides an indication of ability to marry and support a

spouse financially. The report indicated that majority of those who have never been married

are not economically active (63.1%), whereas majority of those who were married are

employed (54.7%).

Nationality and religious affiliation

Almost all the people in the Municipality are Ghanaians by birth (97.5%). Persons with dual

nationality constitute only 1.2 percent and Ghanaians by naturalization form less than one

percent (0.3%). Persons born outside Ghana constitute just about two percent (1.9%).

Christians constitute the majority (88.9%) of the population in the municipality, followed by

Moslems (2.2%). Persons who reported as having no religious affiliation constitute 6.0

percent. The majority (33.5%) of Christians in the Municipality are Protestants (41.1%),

followed by Pentecostal/Charismatic (33.5%) and other Christians (11.2%). Catholics

recorded the lowest proportion of Christians in the Municipality (3.1%).

Literacy and education

On literacy and education, about 85.0 percent of the population aged 11 years and older in the

Municipality are literate. Literacy rate is higher for males (90.1%) than for females (79.1%).

A high percentage (70.2%) of the population in the municipality could read and write both in

English and a Ghanaian language; and 17.6 percent were literate in English only. A little over

eleven percent (11.4%) are literate in Ghanaian language only. Less than one percent of the

population could read and write in English, French and Ghanaian language (0.7).

Of the population currently in school, the majority (87.4%) are in the basic level (nursery,

kindergarten, primary, and JHS). Those in SHS constitute less than one tenth (8.5%) of the

population currently in school. Only 2.9 percent are in the tertiary level with less than one

percent (0.4%) in vocational/technical/commercial schools. The observed pattern is the same

for both males and females. For those who have attended in the past, 46.7 percent of males

and 53.3 percent of females had attended school in the past. The majority of them had

61

attained basic education (75.5%), with only 15.1 percent attaining post middle/SSS/SHS level

of education. Only 5.8 percent had attended tertiary institutions. For the sexes, a higher

percentage of males than females had achieved secondary/SHS education (5.4% versus 3.0%)

and tertiary levels of education (8.3% versus 3.7%). On the contrary, a higher percentage of

the females than males had achieved basic education (79.9% versus 70.7%). This observation

suggests that a higher percentage of the females than males in the municipality discontinue

their education after the basic level of education.

Economic characteristics

Of the population 15 years and older in the municipality, the majority (66.1%) are

economically active. A higher proportion of males (67.7%) than females (64.7%) are

economically active. Among the economically active population, slightly more males

(92.1%) than females (91.7%) are employed. The majority (41.3%) of the economically not

active population are persons in full time education. Young adults (20-29 years) constitute

more than a fifth (24.8%) of the employed population, while approximately a fifth (24.6%) of

adults in the age cohorts 30-39 years are employed. As expected, adolescents (15-19 years)

and young adults (20-29 years) together constitute majority (64.3%) of the unemployed

population. The reason for the unemployment status of this youth may be that most of them

may still be in school or training.

With regards to occupation, three major occupations are common in the Municipality. These,

in order of importance are agriculture, forestry and fishing (37.0%), service and sales (22.1%)

and craft and related work (17.8%). The majority of the workers are into agriculture, forestry

and fishing industry (37.4%), followed by wholesale and retail trade (17.7%) and distantly by

manufacturing (12.0%). For the sexes, a higher percentage of males than females are into

agricultural, forestry and fishing (41.1% against 30.2%). On the other hand, a higher

percentage (33.8%) of females are in the service and sales than males.

The majority (67.5%) of the economically active population are self-employed without

employees. Employees constitute about 18 percent of the working population, with just about

five percent (4.8%) being self-employed with employees. Males (23.5%) are more likely to

be employees than females (11.8%). The reverse is the case for persons who are employed

without employees, where females have a higher percentage (73.6%) than males (60.2%).

Majority (86.1%) of the working population are in private informal sector. The next

important sector is the public (government) sector which employed 9.0 percent of the

workers. Only 4.5 percent of the employed population work in the private formal sector.

Information Communication Technology (ICT)

The use of mobile phones in the Municipality is quite extensive with 47.7 percent of the

population 12 years and older having mobile phones. There are relatively more females than

males having mobile phones (53.1% versus 49.3%). Access of households to ICT facilities

such as computers is low, with less than six percent (5.2%) of households having

desktop/laptop computer(s). The use of internet is also quite low, with only about six percent

of the population aged 12 years and older using internet facilities.

62

Persons with disability (PWDs)

There are 4,097 persons, representing 3.0 percent of the total population with some form of

disability in the Municipality. The female population tends to have higher percentage (3.2%)

of disable persons than males (2.7%). Visual impairment is the most common type of

disability among PWDs (29.0%) in the Municipality, followed closely by physical disabilities

(27.0%). Those with hearing impairment (10.0%) formed the third largest. Other types of

disabilities reported include speech intellectual challenges (9.0%), emotional challenges

(9.0%) and speech impairment (8.0%).

Generally, the level of education of the PWDs in the locality is low. Out of the 4,055 persons

with disability in the municipality, 38.5 percent had never been school, 49.0 percent had

attended basic education, and only 9.7 percent had attended secondary/SSS/SHS and higher

levels of education. The low level of education among this group, compromises their

opportunity for employment and earning for a decent livelihood.

The majority (59.4%) of PWDs are not economically active as indicated in the table. Of the

economically active disable population, the majority (38.2%) are employed. A similar

distributional pattern of economic activity status of PWDs can be observed for both males

and females.

Agricultural activities

The municipality recorded a total of 15,703 agricultural households, representing 47.1 percent of

all households. With regard to locality of residence, there are more agricultural households in

the rural areas (58.6%) than urban areas (27.1%). The major agricultural activity in the

municipality is crop farming (93.5%), followed by livestock rearing (34.6%) and tree

planting (1.7%). Less than one percent of agricultural households are engaged in fish farming

(0.1%). As expected, a higher proportion of rural agricultural households are engaged in crop

farming compared to urban agricultural households (94.9% versus 88.4%). Livestock rearing

was also recorded by a higher percentage of rural agricultural households (36.9%) than the

urban (26.0%).

Housing conditions

The total stock of houses in the municipality is 22,896 of which the highest proportion is

located in rural localities (69.0%). The highest proportion of dwelling units is compound

house (52.1%), followed by separate houses (31.4%). Compound houses (58.7% vs. 48.3%)

and semi-detached houses (9.3% vs. 6.1%) are more common in the urban areas than rural

areas, whereas rural areas have more separate houses (36.4% vs. 22.6%).

Cement blocks/concrete (63.6%) and mud brick/earth (28.7%) are the two main construction

materials used by households for outer walls in the Municipality. The use of cement

blocks/concrete featured most prominently in outer wall construction in the urban than rural

localities (85.6% versus 51.3%), while a substantially high proportion of the outer wall of

dwelling units in the rural areas (40.6%) compared to urban areas (7.5%) were constructed

with brick/earth. The main material used for floors of occupied dwelling units is cement (78.4%), while the main roofing material is metal sheet (91.2%).

In terms of room occupancy by household members, more than half (53.4%) of occupied

dwellings have one sleeping room. The second and third most occupied types of sleeping

rooms in occupied dwellings are two sleeping rooms (26.6%) and three sleeping rooms

63

(10.8%). Occupied dwellings units with four or more rooms constitute close to one-tenth

(9.1%). Higher proportions household sizes between 1-4 members occupied one room, while

households with 5-7 members occupied two rooms. Overall, the data suggest overcrowding

of sleeping arrangements of household members in the Municipality

Close to half (48.0%) of dwelling units in the district are owner occupied, 26.1 percent are

occupied on “rent free” basis (owned by a relative who was not a member of household)

while 22.8 percent of dwelling units are rented. Male-headed households dominate in the

ownership of housing units in the district accounting for 59.3 percent as against 40.7 percent

of female- headed households. However, a higher proportion of female-headed households

than male-headed households occupied dwelling units on “rent free” basis (50.1% versus.

49.9%).

Electricity is the main source of lighting for most households (60.5%) in the Municipality.

The percentage of households using electricity as their main source of lighting exceeds the

regional average of 58.5 percent. This is followed distantly by kerosene lamps (18.8%) with

flashlight/torch (8.5%) placing third. The percentage of urban households using electricity

(82.4%) far exceeds that for rural households (47.9%). More than one third (38.2%) of rural

households use kerosene lamps as their main source of lighting, against 13.2 percent for

urban households.

The majority (26.1%) of households in the Municipality use borehole water as their main

source of drinking water. The next most common source of drinking water is public

standpipes (21.1%), followed by pipes inside dwelling units (12.2%). A little over one tenth

(10.2%) of households depend on sachet water as their main source of drinking water. With

regards to water for other domestic use, the majority (27.4%) of households us borehole

water, followed by river or stream water (23.9%).

The data shows that the main source of energy for cooking for households in the Municipality

is firewood (40.8%) followed by charcoal (39.0%). The proportion of households using gas

is also quite high (16.1%). In rural areas, the main source of cooking fuel is 54.5 percent

compared with 16.9 percent for urban areas. In contrast, charcoal is used by a higher

percentage of urban households (51.4%) compared to rural households (31.9 percent). The

use of wood and charcoal as the main sources of fuel by households has implications for the

management of deforestation in the municipality.

With regard to sanitation in the Municipality, the majority of households use pit latrine

933.2%), followed by public toilet (29.2%). About 5.0 percent of households reported that

they had no toilet facilities and therefore resorted to bush/beach/open fields. The main types

of bathing facilities were shared bathroom in the same house (28.7%), bathroom for exclusive

use of household members (25.9%), and shared open bathing cubicle (25.9%).

On disposal of solid and liquid waste, the most common methods of solid waste disposal used

by households were dumping in a container or dumping unto open dump site (60.7%),

followed by the burning of solid waste by households (21.7%). Routine collection of waste

from houses is hardly practiced (3.4%). About another 6.0 percent of households dumped

their solid wastes indiscriminately. For liquid waste disposal, the most common method used

by households is throwing the waste onto the compound, followed by throwing it onto the

street or outside the house (24.4%) and throwing into the gutter (18.1%). A substantially low

proportion of households disposed of their liquid waste through a sewerage system (0.2%).

64

9.3 Conclusions and policy implications

Demographic analysis of the municipality’s population showed that majority of the population live in rural areas, reflecting its rural nature and the need to allocate more

resources for rural development in the Municipal Assembly. The age structure of the

population is also youthful with a high potential for growth, particularly in the context of the

high fertility and relatively low education levels among women in the municipality. To

manage future population growth, there is the need to promote for family life education and

also strengthen family planning services in the municipality.

The social characteristics of the population revealed that households in the municipality were

predominantly male-headed, supporting the persistence of the traditional marriage and family

system where the male is the head and breadwinner of the household. Although the average

household size of four persons per household in the municipality is not large, a significant

proportion of households had eight more members which have implication for the distribution

of household resources for basic needs such food, health, education and clothing and

consequently poverty reduction. Another important finding is the high proportion of the

population never married among adolescent and young adults, suggesting that young people

in the municipality are delaying marriage to acquire education. This provides a window of

opportunity for the municipality to improve its human resources for socio-economic

development. The report also shows that a significant proportion of elderly population was

widowed. This finding has implication for policy measures aimed at the provision of social

services for the aged in the municipality.

Most individuals in the district are literate with higher percentage of males than females

being literate. School attendance at the basic level of education is also high, although

universal attendance has not yet been achieved. However, participation in post-basic level of

education is low, particularly among the female population. This observation has implications

for the education of girls in the municipality regarding Government’s policy to promote gender equity in access to education.

Majority of the workforce in the municipality was self-employed who work in the private

sector, indicating that the economy in the municipality is dominated by small-scale

enterprises with little avenue for creating jobs for other people. Furthermore, most of the

private sector businesses operating in the municipality are sole proprietors and not properly

registered, making it difficult to monitor them for the purpose of revenue mobilization. The

Municipal Assembly need to intensify the registration of all small-scale enterprises operating

in the municipality for optimum revenue collection, while at the same time assisting these

enterprises to access credit for expansion and employment generation.

Generally, the participation rate of PWDs in education and the economy were low compared

to those non-PWDs. To enhance educational opportunities for PWDs, policy measures should

be taken to expand and improve the provision of specialized schools for children with

disability in the district. To increase job opportunities for PWDs, employment policies in the

municipality need to pay attention to the building and equipping of skill development training

centers to train PWDs in various skills that will make them employable and reduce their

vulnerability.

The use of mobile phones is quite extensive in the municipality covering over fifty percent of

the population aged 12 years and older. However, the poor access to the internet recorded

indicates a digital divide between the municipality and the rest of the world. Concerted

65

efforts are therefore needed to close this gap. In order to increase access to the Internet, the

municipality needs to set up internet centers in public places such as the community center

and the library as part of its development plan.

The report has shown that housing condition in the municipality was generally poor in 2010.

Access of households to good drinking water was quite inadequate with over half of

households not having access to improved water sources such as pipe- borne water and

boreholes. Housing conditions were also poor in other areas including access to drainage and

improved toilet and bathing facilities. There is the need for the municipality to device

strategies to promote the construction of water and sanitation facilities such as boreholes and

domestic latrines and bathing facilities in collaboration with the relevant stakeholders.

The disposal of both solid and liquid household waste in the municipality was inadequate.

Investment in waste disposal and sanitation should be seen as part of public health measures

in the municipality. Sanitation by-laws, for example, should also be enforced at the

community and households levels.

On access to cooking fuel, households in the municipality relied mainly on firewood and

charcoal for cooking, with the use of gas limited to a substantially low proportion of

households. The reliance on firewood and charcoal has implications for environmental

management, particularly in the area of the control of deforestation in the municipality. There

is therefore the need by the Municipal Assembly to devise strategies and measures to promote

and encourage households to shift to the use of gas as fuel for cooking.

66

REFERENCES

Ghana Statistical Service, 2010, Eastern Regional Analytical Report 2010 Population and

Housing Censuses

Ghana Statistical Service, National Analytical Report 2010 Population and Housing Census

67

APPENDICES

Table 1A: Household composition by type of locality

Household composition Number

Number

Total Urban Rural

Total 33,322

33,322 12,146 21,176

Household with head and a spouse only 1,013

1,013 379 634

Household with head spouse(s) and biological/adopted

children only 6,645

6,645 1,973 4,672

Household with head spouse(s) biological/adopted children

and relatives of the head only 3,301

3,301 899 2,402

Household with head spouse(s) biological/adopted children

relatives and nonrelatives of the head 228

228 82 146

Household with head spouse(s) and other composition 1,069

1,069 399 670

Head only 6,800

6,800 2,774 4,026

Household with head and biological/adopted children only 4,704

4,704 1,713 2,991

Household with head biological/adopted children and

relatives of the head only 4,826

4,826 1,866 2,960

Household with head biological/adopted children relatives

and nonrelatives of the head 293

293 137 156

Household with head and other composition but no spouse 4,443 4,443 1,924 2,519 Source: Ghana Statistical Service, 2010 Population and Housing Census

68

Table 2A: Population 3 years and older by sex, disability type and level

of education

Sex/Type Total

Never

attended

Nursery

Kinder-

garten

Primary

JSS/

JHS

Middle

SSS/

SHS

Secon-

dary

Voc/

Tec/

Comm

Post

middle/

secondary

certificate

Post-

secondary

diploma

Bachelor ‘s

degree

Post graduate

(Cert. Diploma

Masters PHD

etc.)

Total 126,248 18877 2,502 7,914 34,465 24,724 17,556 9,145 2,397 2,243 1,674 2,489 1,854 408

No disability 122,193 17314 2,485 7,859 33,793 24,431 16,588 9,053 2,289 2,148 1,599 2,430 1,806 398

With a disability 4,055 1563 17 55 672 293 968 92 108 95 75 59 48 10

Sight 1,536 586 9 16 234 106 377 45 41 33 30 29 26 4

Hearing 543 259 - 13 84 34 111 9 10 10 5 3 3 2

Speech 439 219 6 14 68 26 69 10 9 6 8 2 2 -

Physical 1,414 615 5 12 197 70 363 15 35 35 33 20 14 -

Intellectual 453 224 2 7 76 22 87 6 20 5 2 2 - -

Emotional 494 219 3 1 70 44 118 11 10 8 5 4 1 -

Other 410 128 - 8 90 42 88 11 9 16 7 2 5 4

Male

Total 58,814 5934 1,249 4,159 15,922 12,009 8,307 4,927 1,477 1,022 797 1,528 1,206 277

No disability 57,087 5497 1,234 4,121 15,633 11,858 7,833 4,875 1,397 963 745 1,492 1,168 271

With a disability 1,727 437 15 38 289 151 474 52 80 59 52 36 38 6

Sight 644 165 7 12 90 49 181 27 31 27 16 15 22 2

Hearing 241 91 - 6 48 20 52 7 9 3 2 1 2 -

Speech 225 89 4 5 49 17 34 7 9 2 5 2 2 -

Physical 582 160 5 9 66 38 206 6 21 21 24 16 10 -

Intellectual 202 79 2 6 43 11 37 4 16 3 - 1 - -

Emotional 197 72 3 1 22 23 56 5 9 1 4 - 1 -

Other 192 39 - 8 43 20 46 5 7 11 4 2 3 4

Female

Total 67,434 12943 1,253 3,755 18,543 12,715 9,249 4,218 920 1,221 877 961 648 131

No disability 65,106 11817 1,251 3,738 18,160 12,573 8,755 4,178 892 1,185 854 938 638 127

With a disability 2,328 1126 2 17 383 142 494 40 28 36 23 23 10 4

Sight 892 421 2 4 144 57 196 18 10 6 14 14 4 2

Hearing 302 168 - 7 36 14 59 2 1 7 3 2 1 2

Speech 214 130 2 9 19 9 35 3 - 4 3 - - -

Physical 832 455 - 3 131 32 157 9 14 14 9 4 4 -

Intellectual 251 145 - 1 33 11 50 2 4 2 2 1 - -

Emotional 297 147 - - 48 21 62 6 1 7 1 4 - -

Other 218 89 - - 47 22 42 6 2 5 3 - 2 -

69

Table 3A: Population by sex, number of households and houses in the

20 largest communities

S/No. Community Name

Sex House

holds Houses Total Male Female

1 Akropong 12,822 5,856 6,966 3,498 1,767

2 Mampong 10,404 4,761 5,643 2,474 1,332

3 Larteh 10,175 4,502 5,673 2,750 1,999

4 Adukrom 7,925 3,445 4,480 2,058 1,232

5 Mamfe 5,236 2,313 2,923 1,366 758

6 Okorase 4,814 2,322 2,492 1,204 873

7 Tutu 4,561 2,077 2,484 1,189 712

8 Abiriw 4,301 1,873 2,428 1,138 537

9 Adawso 3,903 1,857 2,046 920 579

10 Obosomase 3,372 1,557 1,815 868 522

11 Amanokrom 3,164 1,346 1,818 801 513

12 Apirede 2,743 1,163 1,580 682 465

13 Dawu 2,696 1,250 1,446 745 401

14 Awukugua 2,600 1,146 1,454 713 455

15 Tinkong 1,775 879 896 412 258

16 New Mangoase 1,765 867 898 472 338

17 Okra Kwadwo 1,737 869 868 398 404

18 Asenema 1,630 797 833 380 281

19 Kwamoso 1,512 744 768 425 323

20 14 Miles (Mintakrom) 1,269 606 663 282 266

70

Table 4A: Population by age group in the 20 largest communities

S/No. Community Name

Age Group

All

ages 0-4 5-9 10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75+

1 Akropong 12,822 1,376 1,286 1,380 1,476 1,127 963 799 676 607 557 542 413 376 271 325 648

2 Mampong 10,404 1,096 1,042 1,196 1,101 958 787 694 580 502 481 474 329 306 203 228 427

3 Larteh 10,175 1,145 1,210 1,309 966 643 637 524 513 443 432 432 294 357 266 304 700

4 Adukrom 7,925 860 962 991 923 671 563 466 405 329 301 320 186 226 145 202 375

5 Mamfe 5,236 568 505 543 519 497 426 359 305 248 202 189 145 176 116 151 287

6 Okorase 4,814 697 517 473 481 506 488 355 319 213 176 156 114 118 55 79 67

7 Tutu 4,561 606 585 521 432 404 339 275 254 209 160 161 119 116 90 108 182

8 Abiriw 4,301 500 493 507 484 315 325 261 209 181 175 163 138 123 85 125 217

9 Adawso 3,903 575 465 568 408 301 312 235 196 178 137 154 80 86 50 56 102

10 Obosomase 3,372 442 411 444 331 281 246 218 176 133 134 110 87 88 69 80 122

11 Amanokrom 3,164 338 323 356 298 266 264 230 174 166 129 157 89 85 81 66 142

12 Apirede 2,743 326 310 314 296 227 183 128 118 92 112 109 94 86 66 87 195

13 Dawu 2,696 355 305 350 276 203 212 148 187 101 88 108 67 68 38 57 133

14 Awukugua 2,600 354 285 329 275 207 201 159 149 115 101 82 60 59 47 70 107

15 Tinkong 1,775 243 226 220 193 133 127 136 80 70 67 64 54 48 27 41 46

16 New Mangoase 1,765 244 210 216 199 148 120 94 87 82 47 76 74 59 34 24 51

17 Okra Kwadwo 1,737 227 210 214 178 153 130 122 84 80 77 69 50 44 25 31 43

18 Asenema 1,630 223 219 181 184 136 141 105 117 72 46 47 22 52 24 22 39

19 Kwamoso 1,512 201 211 174 125 119 106 105 87 63 75 75 54 43 30 15 29

20

14 Miles

(Mintakrom) 1,269 179 186 151 117 114 91 89 69 57 39 39 45 22 12 24 35

71

LIST OF CONTRIBUTORS

Project Secretariat

Dr. Philomena Nyarko, Government Statistician

Mr. Baah Wadieh, Deputy Government Statistician

Mr. David Yenukwa Kombat, Acting Census Coordinator

Mr. Sylvester Gyamfi, DISDAP Project Coordinator

Mrs. Abena A. Osei-Akoto, Data Processing

Mr. Rochester Appiah Kubi Boateng, Data Processing

Mrs. Jacqueline Anum, Data Processing

Mrs. Samilia Mintah, Data Processing

Mr. Yaw Misefa, Data Processing

Mr. Ernest Enyan, Data Processing

Mr. Kobina Abaka Ansah, Regional Statistician

Ms. Hanna Frempong Konadu, Formatting/Typesetting

Ms. Justina Yeboah, Formatting/Typesetting

Writers

Mr. Robert Lawson

Mr. Henry Sen-Opoku

Consultant

Dr. Martin Yeboah

Editor/ Reviewers

Prof. N.N.N. Nsowah-Nuamah

Mr. David Yenukwa Kombat

Mr. Vitus Bobrnuo

Sampling and MOE__Jan__14__2020__01++-1.ppt

*


SAMPLING _PROCEDURES, METHODS and/or TECHNIQUES

LEARNING OBJECTIVES

*

  • Learn the reasons for sampling
  • Develop an understanding about different sampling methods
  • Distinguish between probability & non probability sampling
  • Discuss the relative advantages & disadvantages of each sampling methods

What is research?

*

“Scientific research is systematic, controlled, empirical, and critical investigation of natural phenomena guided by theory and hypotheses about the presumed relations among such phenomena.”

Kerlinger, 1986

Research is an organized and systematic way of finding answers to questions

Important Components of Empirical Research

*

Problem statement, research questions, purposes, benefits

Theory, assumptions, background literature

Variables and hypotheses

Operational definitions and measurement

Research design and methodology

Instrumentation, sampling

Data analysis

Conclusions, interpretations, recommendations

*

PROBLEM STATEMENT, PURPOSES, BENEFITS

What exactly do I want to find out?

What is a researchable problem?

What are the obstacles in terms of knowledge, data availability, time, or resources?

Do the benefits outweigh the costs?

 

THEORY, ASSUMPTIONS, BACKGROUND LITERATURE

What does the relevant literature in the field indicate about this problem?

Which theory or conceptual framework does the work fit within?

What are the criticisms of this approach, or how does it constrain the research process?

What do I know for certain about this area?

What is the background to the problem that needs to be made available in reporting the work?

 

VARIABLES AND HYPOTHESES

What will I take as given in the environment ie what is the starting point?

Which are the independent and which are the dependent variables?

Are there control variables?

Is the hypothesis specific enough to be researchable yet still meaningful?

How certain am I of the relationship(s) between variables?

 

OPERATIONAL DEFINITIONS AND MEASUREMENT

Does the problem need scoping/simplifying to make it achievable?

What and how will the variables be measured?

What degree of error in the findings is tolerable?

Is the approach defendable?

 

RESEARCH DESIGN AND METHODOLOGY

What is my overall strategy for doing this research?

Will this design permit me to answer the research question?

What constraints will the approach place on the work?

 

INSTRUMENTATION/SAMPLING

How will I get the data I need to test my hypothesis?

What tools or devices will I use to make or record observations?

Are valid and reliable instruments available, or must I construct my own?

How will I choose the sample?

Am I interested in representativeness?

If so, of whom or what, and with what degree of accuracy or level of confidence?

 

DATA ANALYSIS

What combinations of analytical and statistical process will be applied to the data?

Which of these will allow me to accept or reject my hypotheses?

Do the findings show numerical differences, and are those differences important?

 

CONCLUSIONS, INTERPRETATIONS, RECOMMENDATIONS

Was my initial hypothesis supported?

What if my findings are negative?

What are the implications of my findings for the theory base, for the background assumptions, or relevant literature?

What recommendations result from the work?

What suggestions can I make for further research on this topic?

SAMPLING

*

A sample is “a smaller (but hopefully representative) collection of units from a population used to determine truths about that population” (Field, 2005).

Why sample?

  • Resources (time, money) and workload
  • Gives results with known accuracy that can be calculated mathematically

The sampling frame is the list from which the potential respondents are drawn

  • Registrar’s office
  • Class rosters
  • Ministry of Mines and Energy, etc.

*

Sampling frame errors: university versus personal email addresses; changing class rosters; are all students in your population of interest represented?

SAMPLING……

*

What is your population of interest?

  • To whom do you want to generalize your results?
  • All doctors
  • School children
  • An ethnic group?
  • Women aged 15-45 years
  • Etc

  • Can you sample the entire population?

*

How do we determine our population of interest?

  • Administrators can tell us
  • We notice anecdotally or through qualitative research that a particular subgroup of students is experiencing higher risk
  • We decide to do everyone and go from there

3 factors that influence sample representativeness

  • Sampling procedure
  • Sample size
  • Participation (response)

When might you sample the entire population?

  • When your population is very small
  • When you have extensive resources
  • When you don’t expect a very high response

SAMPLING…….

*

3 factors that influence sample representative-ness

  • Sampling procedure
  • Sample size
  • Participation (response)
  • When might you sample the entire population?
  • When your population is very small
  • When you have extensive resources
  • When you don’t expect a very high response

*

SAMPLING BREAKDOWN

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Picture of sampling breakdown

SAMPLING…….

*

TARGET POPULATION

STUDY POPULATION

SAMPLE

Types of Samples

*

Probability (Random) Samples

  • Simple random sample
  • Systematic random sample
  • Stratified random sample
  • Multistage sample
  • Multiphase sample
  • Cluster sample

  • Non-Probability Samples
  • Convenience sample
  • Purposive sample
  • Quota

*

Two general approaches to sampling are used in social science research. With probability sampling, all elements (e.g., persons, households) in the population have some opportunity of being included in the sample, and the mathematical probability that any one of them will be selected can be calculated. With nonprobability sampling, in contrast, population elements are selected on the basis of their availability (e.g., because they volunteered) or because of the researcher's personal judgment that they are representative. The consequence is that an unknown portion of the population is excluded (e.g., those who did not volunteer). One of the most common types of nonprobability sample is called a convenience sample – not because such samples are necessarily easy to recruit, but because the researcher uses whatever individuals are available rather than selecting from the entire population.

Because some members of the population have no chance of being sampled, the extent to which a convenience sample – regardless of its size – actually represents the entire population cannot be known

Population definition

*

A population can be defined as including all people or items with the characteristic one wishes to understand.

  • Because there is very rarely enough time or money to gather information from everyone or everything in a population, the goal becomes finding a representative sample (or subset) of that population.

Population definition……. Cont’n

*

  • Note also that the population from which the sample is drawn may not be the same as the population about which we actually want information. Often there is large but not complete overlap between these two groups due to frame issues etc .
  • Sometimes they may be entirely separate - for instance, we might study records from people born in 2008 in order to make predictions about people born in 2009.

SAMPLING FRAME

*

  • In the most straightforward case, such as the sentencing of a batch of material from production (acceptance sampling by lots), it is possible to identify and measure every single item in the population and to include any one of them in our sample. However, in the more general case this is not possible. There is no way to identify all rats in the set of all rats. Where voting is not compulsory, there is no way to identify which people will actually vote at a forthcoming election (in advance of the election)
  • As a remedy, we seek a sampling frame which has the property that we can identify every single element and include any in our sample .

The sampling frame must be representative of the population

PROBABILITY SAMPLING

A probability sampling scheme is one in which every unit in the population has a chance (greater than zero) of being selected in the sample, and this probability can be accurately determined.

When every element in the population does have the same probability of selection, this is known as an 'equal probability of selection' (EPS) design. Such designs are also referred to as 'self-weighting' because all sampled units are given the same weight.

To achieve this the sampling frame used needs to:

ensure that the correct population is being sampled i.e. it addresses the questions of interest

accurately covers all members of the population being studied so they have a chance to be sampled.

The quality of the population list (sampling frame) i.e. whether it is up-to-date and complete is the most important feature for accuracy in the sampling.

Probability Sampling in Quantitative Research

Click to add notes

Why is probability sampling important in quantitative research?

Research finding not based on samples are biased / unrepresentative.

Based on a sampling frame it enables research to be replicable or repeatable.

Research results can be projected from the sample to the larger population with known levels of certainty/precision (i.e. standard errors & confidence intervals for survey estimates can be constructed).

Probability Sampling in Quantitative Research

For criterion definitions see Slide Series 1: Quantitative Approaches to Research (slide 19, Key Criteria Terminology).

Process

*

The sampling process comprises several stages:

Defining the population of concern

Specifying a sampling frame, a set of items or events possible to measure

Specifying a sampling method for selecting items or events from the frame

Determining the sample size

Implementing the sampling plan

Sampling and data collecting

Reviewing the sampling process

  • Systematic error (or bias)

i. Inaccurate response (information bias)

ii. Selection bias

Errors in sample

PROBABILITY SAMPLING…….

*

Probability sampling includes:

  • Simple Random Sampling,
  • Systematic Sampling,
  • Stratified Random Sampling,
  • Cluster Sampling
  • Multistage Sampling.
  • Multiphase sampling

SIMPLE RANDOM SAMPLING

Applicable when population is small, homogeneous & readily available

All subsets of the frame are given an equal probability. Each element of the frame thus has an equal probability of selection.

It provides for greatest number of possible samples. This is done by assigning a number to each unit in the sampling frame.

A table of random number or lottery system is used to determine which units are to be selected.

Stages in random sampling:

SIMPLE RANDOM SAMPLING……..

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Advantages

Estimates are easy to calculate.

Disadvantages

If sampling frame large, this method is impracticable.

Minority subgroups of interest in population may not be present in sample in sufficient numbers for study.

REPLACEMENT OF SELECTED UNITS

*

Sampling schemes may be without replacement ('WOR' - no element can be selected more than once in the same sample) or with replacement ('WR' - an element may appear multiple times in the one sample).

  • For example, if we catch fish, measure them, and immediately return them to the water before continuing with the sample, this is a WR design, because we might end up catching and measuring the same fish more than once. However, if we do not return the fish to the water (e.g. if we eat the fish), this becomes a WOR design.

SYSTEMATIC SAMPLING

Systematic sampling relies on arranging the target population according to some ordering scheme and then selecting elements at regular intervals through that ordered list.

Systematic sampling involves a random start and then proceeds with the selection of every kth element from then onwards. In this case, k=(population size/sample size).

It is important that the starting point is not automatically the first in the list, but is instead randomly chosen from within the first to the kth element in the list.

A simple example would be to select every 10th name from the telephone directory (an 'every 10th' sample, also referred to as 'sampling with a skip of 10').

*

Systematic Sampling Procedure

List all the units in the population from 1,2,…,N

– Sampling frame

  • Select a random number g in the interval
  • 1 g K, using a random mechanism e.g. random number tables,

where K =

Where:

K is called the Sampling Interval

N is the population size; n is the sample size

  • The random number g is called the random start and constitutes the first unit of the sample

*

Systematic Sampling Procedure

  • Take every kth unit after the random start
  • The selected units will be:

g, g+k, g+2k, g+3k, g+4k, …,g+(n-1)k

  • Until we have n units
  • Example N =10000, n=100

k = =100

  • Suppose g=87

*

Systematic Sampling Procedure

We select the following units

87, 187, 287, 387,…, 9987

NB: This procedure is however only valid if k is an integer (whole number)

  • If k is not an integer (whole number) there are a number of methods we can use. We will consider just one of them.

*

Systematic Sampling Procedure

One suitable suggestion is to choose the integer

k closest to the ratio

  • Method: Use Fractional Intervals
  • Suppose we want to select a sample of 100 units from a population of 21,156.
  • Calculate k = =211.56
  • Select a random start g between 1 and 21156 using a random mechanism.

*

Systematic Sampling Procedure

Suppose g = 582

  • Add the interval 21156 successively obtaining exactly 100 numbers
  • The numbers will be 582, 21738, 42894, …

  • Divide each number by 100 and round to the nearest whole number to get the selected sample, i.e.
  • 6, 217, 429, etc

SYSTEMATIC SAMPLING……

*

ADVANTAGES:

Sample easy to select

Suitable sampling frame can be identified easily

Sample evenly spread over entire reference population

DISADVANTAGES:

Sample may be biased if hidden periodicity in population coincides with that of selection.

Difficult to assess precision of estimate from one survey.

STRATIFIED SAMPLING

*

Where population embraces a number of distinct categories, the frame can be organized into separate "strata." Each stratum is then sampled as an independent sub-population, out of which individual elements can be randomly selected.

Every unit in a stratum has same chance of being selected.

Using same sampling fraction for all strata ensures proportionate representation in the sample.

Adequate representation of minority subgroups of interest can be ensured by stratification & varying sampling fraction between strata as required.

STRATIFIED SAMPLING……

Finally, since each stratum is treated as an independent population, different sampling approaches can be applied to different strata.

Drawbacks to using stratified sampling.

First, sampling frame of entire population has to be prepared separately for each stratum

Second, when examining multiple criteria, stratifying variables may be related to some, but not to others, further complicating the design, and potentially reducing the utility of the strata.

  • Finally, in some cases (such as designs with a large number of strata, or those with a specified minimum sample size per group), stratified sampling can potentially require a larger sample than would other methods

Gold standard’ of sampling.

Why? Designed to be more representative of the population where the sampling frame is ‘stratified’ according to population variables .

Variables selected for stratifying are determined by the characteristics needed by the research.

Stratification – splitting the population into the different strata (variables e.g. gender, age, ethnic background).

Samples can be stratified across more than one variable.

Stratified Random Sample

Click to add notes

STRATIFIED SAMPLING…….

*

Draw a sample from each stratum

POST-STRATIFICATION

*

Stratification is sometimes introduced after the sampling phase in a process called "post-stratification“.

This approach is typically implemented due to a lack of prior knowledge of an appropriate stratifying variable or when the experimenter lacks the necessary information to create a stratifying variable during the sampling phase. Although the method is susceptible to the pitfalls of post hoc approaches, it can provide several benefits in the right situation. Implementation usually follows a simple random sample. In addition to allowing for stratification on an ancillary variable, post-stratification can be used to implement weighting, which can improve the precision of a sample's estimates.

CLUSTER SAMPLING

Cluster sampling is an example of 'two-stage sampling' .

First stage a sample of areas is chosen;

Second stage a sample of respondents within those areas is selected.

Population divided into clusters of homogeneous units, usually based on geographical contiguity__ (contact or proximity).

Sampling units are groups rather than individuals.

A sample of such clusters is then selected.

All units from the selected clusters are studied.

CLUSTER SAMPLING…….

*

Advantages:

Cuts down on the cost of preparing a sampling frame.

This can reduce travel and other administrative costs.

Disadvantages:

sampling error is higher for a simple random sample of same size.

Often used to evaluate vaccination coverage

CLUSTER SAMPLING…….

Identification of clusters

List all cities, towns, villages & wards of cities with their population falling in target area under study.

Calculate cumulative population & divide by for example 30, this gives sampling interval.

Select a random no. less than or equal to sampling interval having same no. of digits. This forms 1st cluster.

Random no.+ sampling interval = population of 2nd cluster.

Second cluster + sampling interval = 3rd cluster.

Last or 30th cluster = 29th cluster + sampling interval

CLUSTER SAMPLING…….

*

Two types of cluster sampling methods.

One-stage sampling.

All of the elements within selected clusters are included in the sample.

Two-stage sampling.

A subset of elements within selected clusters are randomly selected for inclusion in the sample.



CLUSTER SAMPLING…….

In cluster sampling, we follow these steps:

  • divide population into clusters (usually along geographic boundaries)

  • randomly sample clusters

  • measure all units within sampled clusters

*

Cluster sampling

Section 4

Section 5

Section 3

Section 2

Section 1

*

The total number of clusters is given by the formula:

Total number of clusters = Total number of households in sample

Cluster size

Example:

25 minutes per house-hold to administer a questionnaire

Five minutes to move in-between house-holds

=>

﴾ 6.5 hours x 60 minutes ﴿ ÷ 25 minutes/household ~ 15 This become the cluster size.

day hour

Working 6.5 hours per day __ researcher covers 15 houses per day



Cluster sampling

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

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Difference Between Strata and Clusters

Although strata and clusters are both non-overlapping subsets of the population, they differ in several ways.

All strata are represented in the sample; but only a subset of clusters are in the sample.

With stratified sampling, the best survey results occur when elements within strata are internally homogeneous. However, with cluster sampling, the best results occur when elements within clusters are internally heterogeneous

















MULTISTAGE SAMPLING

*

  • Complex form of cluster sampling in which two or more levels of units are embedded one in the other.
  • First stage, random number of districts chosen in all

region/states.

  • Followed by random number of villages.

  • Then third stage units will be houses.

  • All ultimate units (houses, for instance) selected at last step are surveyed.

MULTISTAGE SAMPLING……..

*

  • This technique, is essentially the process of taking random samples of preceding random samples.
  • Not as effective as true random sampling, but probably solves more of the problems inherent to random sampling.
  • An effective strategy because it banks on multiple randomizations. As such, extremely useful.

Multistage sampling used frequently when a complete list of all members of the population not exists and is inappropriate.

  • Moreover, by avoiding the use of all sample units in all selected clusters, multistage sampling avoids the large, and perhaps unnecessary, costs associated with traditional cluster sampling.

MULTISTAGE SAMPLING……..

*

Research Methodology and Design ___The Sample Size

Depends on three factors:

Example:

The estimated prevalence of the variable of interest _ e.g. Pregnant women within a theatre of operation;

The desired level of confidence;

The acceptable margin of error

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

*

Research Methodology and Design ____ The Sample Size


Simple random sample, the sample size required is calculated according to this formula:

n = t2 x p(1-p)

e2

Where:

n = Required sample size

t = Confidence level at 95%

P = Estimated prevalence of pregnancy in the study area

e = Margin of error at 5% ( standard value of 0.05)

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

*

Research Methodology and Design ____ The Sample Size


Simple random sample, the sample size required is calculated according to this formula:

n = t2 x p(1-p)

e2

p (data) can be taken obtain from published reports (Secondary Source): Health centres, Government statistical reports/UNDP etc. Example 20% of national population.

Population of the study area is 124, 050

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

*

Research Methodology and Design ____ The Sample Size


Simple random sample, the sample size required is calculated according to this formula:

Thus n is:

n = t2 x p(1-p)

e2

t = 95% z-score = 1.96

P = 20% => 20/100 = 0.2

e = 5% => 5/100 = 0.05

n = 1.962 * 0.2(1 – 0.2)

0.052

n = 1.962 * 0.2(0.8)

0.052

n = 1.962 * 0.16

0.052

*

Research Methodology and Design ____ The Sample Size


Simple random sample, the sample size required is calculated according to this formula:

Thus n is:

n = t2 x p(1-p)

e2

n = 3.8416 * 0.16

0.052

n = 0.614656

0.0025

n = 245.8624

n = 246

*

Research Methodology and Design ____ The Sample Size


Simple random sample, the sample size required is calculated according to this formula:

Thus n is:

n = t2 x p(1-p)

e2

t = 95% => Z-score = 1.96

P = 15% => 15/100 = 0.15

e = 5% => 5/100 = 0.05

n = 1.962 * 0.15(1 - 0.15)

0.052

n = 1.962 * 0.15(0.85)

0.052

*

Research Methodology and Design ____ The Sample Size

Simple random sample, the sample size required is calculated according to this formula:

Thus n is:

n = t2 x p(1-p)

e2

n = 1.962 * 0.1275

0.052

n = 3.8416 * 0.1275

0.0025

n = 0.489804

0.0025

n = 195.9216

n = 196

*

Research Methodology and Design ____ The Sample Size

Accordingly, the study will adopt sample size determination formula as used by Dahiru, Aliyu and Kene (2006). The formula is specified as follows:

n = z2  (p)(1-p)

              e2 

The sample size will be derived by computing the minimum sample size required for accuracy in estimating proportions of the population using the standard normal deviation set at 95% confidence level (1.96), percentage picking a choice or response (90% = 0.9) and the significant level (0.05 = ±5). In order to obtain a manageable sample size for the study the percentage picking choice is set at 90%.

Source: Dahiru, 1T. Aliyu, A and. Kene, S., (2006). Statistics in Medical Research: Misuse of Sampling and Sample Size Determination. Annals of African Medicine 5(3). 158 – 161

Research Methodology and Design ____ The Sample Size

Running off this figure to the nearest whole number the sample size to be used for this study will be 138 persons

Research Methodology and Design ____ The Sample Size

The sample size is calculated based on the mathematical formula espoused below (Turner, 2003). The sample frame is obtained from the Ministry of Lands and Natural Resources. A sampling frame is defined as the set of source materials from which the sample is selected (Miller & Brewer, 2003).

The sample size is determined at 95 percent significant level:

Turner, A.G. (2003). Sample frames and master Samples. New York, USA: United Nations Secretariat Statistics Division.

Miller, R. L., Brewer, J. D. (2003). A-Z of Social Research - Dictionary of Key Social Science. London, United Kingdom: Sage Publications.

Research Methodology and Design ____ The Sample Size

The sample size is calculated as:

n = 244 (1)

1 + 244 (0.05)²

244 (2)

1 + 244 (.0025)

244 (3)

1 + .61

Research Methodology and Design ____ The Sample Size

The sample size is calculated as:

n =

244 (4)

1.61

151.552795 (5)

n = 152

Research Methodology and Design ____ The Sample Size (In class Exercise)

The sample size is calculated based on the mathematical formula espoused below (Turner, 2003). The sample frame is obtained from the Ministry of Lands and Natural Resources. A sampling frame is defined as the set of source materials from which the sample is selected (Miller & Brewer, 2003).

The sample size is determined at 95 percent significant level:

Research Methodology and Design ____ The Sample Size

The sample size is calculated as:

n = 350 (1)

1 + 350 (0.05)²

350 (2)

1 + 350 (.0025)

350 (3)

1 + .875

Research Methodology and Design ____ The Sample Size

The sample size is calculated as:

n =

350 (4)

1.875

186.6666667 (5)

n = 187

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PROBLEM STATEMENT, PURPOSES, BENEFITS

What exactly do I want to find out?

What is a researchable problem?

What are the obstacles in terms of knowledge, data availability, time, or resources?

Do the benefits outweigh the costs?

 

THEORY, ASSUMPTIONS, BACKGROUND LITERATURE

What does the relevant literature in the field indicate about this problem?

Which theory or conceptual framework does the work fit within?

What are the criticisms of this approach, or how does it constrain the research process?

What do I know for certain about this area?

What is the background to the problem that needs to be made available in reporting the work?

 

VARIABLES AND HYPOTHESES

What will I take as given in the environment ie what is the starting point?

Which are the independent and which are the dependent variables?

Are there control variables?

Is the hypothesis specific enough to be researchable yet still meaningful?

How certain am I of the relationship(s) between variables?

 

OPERATIONAL DEFINITIONS AND MEASUREMENT

Does the problem need scoping/simplifying to make it achievable?

What and how will the variables be measured?

What degree of error in the findings is tolerable?

Is the approach defendable?

 

RESEARCH DESIGN AND METHODOLOGY

What is my overall strategy for doing this research?

Will this design permit me to answer the research question?

What constraints will the approach place on the work?

 

INSTRUMENTATION/SAMPLING

How will I get the data I need to test my hypothesis?

What tools or devices will I use to make or record observations?

Are valid and reliable instruments available, or must I construct my own?

How will I choose the sample?

Am I interested in representativeness?

If so, of whom or what, and with what degree of accuracy or level of confidence?

 

DATA ANALYSIS

What combinations of analytical and statistical process will be applied to the data?

Which of these will allow me to accept or reject my hypotheses?

Do the findings show numerical differences, and are those differences important?

 

CONCLUSIONS, INTERPRETATIONS, RECOMMENDATIONS

Was my initial hypothesis supported?

What if my findings are negative?

What are the implications of my findings for the theory base, for the background assumptions, or relevant literature?

What recommendations result from the work?

What suggestions can I make for further research on this topic?

*

Sampling frame errors: university versus personal email addresses; changing class rosters; are all students in your population of interest represented?

*

How do we determine our population of interest?

  • Administrators can tell us
  • We notice anecdotally or through qualitative research that a particular subgroup of students is experiencing higher risk
  • We decide to do everyone and go from there

3 factors that influence sample representativeness

  • Sampling procedure
  • Sample size
  • Participation (response)

When might you sample the entire population?

  • When your population is very small
  • When you have extensive resources
  • When you don’t expect a very high response

*

Picture of sampling breakdown

*

Two general approaches to sampling are used in social science research. With probability sampling, all elements (e.g., persons, households) in the population have some opportunity of being included in the sample, and the mathematical probability that any one of them will be selected can be calculated. With nonprobability sampling, in contrast, population elements are selected on the basis of their availability (e.g., because they volunteered) or because of the researcher's personal judgment that they are representative. The consequence is that an unknown portion of the population is excluded (e.g., those who did not volunteer). One of the most common types of nonprobability sample is called a convenience sample – not because such samples are necessarily easy to recruit, but because the researcher uses whatever individuals are available rather than selecting from the entire population.

Because some members of the population have no chance of being sampled, the extent to which a convenience sample – regardless of its size – actually represents the entire population cannot be known

Click to add notes

For criterion definitions see Slide Series 1: Quantitative Approaches to Research (slide 19, Key Criteria Terminology).

Click to add notes

*

*

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

*

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

*

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

*

Source: http://ideas.repec.org/p/gbl/wpaper/200706.html

*

*

*

*

*

£

N

n

10000

100

N

n

21156

100

Where:

z = standard normal deviation set at 95% confidence level = 1.96

p = percentage picking a choice or response = 0.9

e = margin of error = 0.05

n = (1.96)

2

(.9) (.1) = 138.29 persons

0.05

2

Assignment 2.docx

Assignment 1

Write a research proposal on the topic below

Assessment of the effect of digitization on client satisfaction in a public hospital: A case study of Tetteh Quarshie Memorial Hospital

Submission date: 25th September, 2020

Requirements

· Not more than 15 pages excluding references

· Times new roman; font size 12

· Spacing 1.5;

· Involves three chapters i.e. 1, 2 & 3

· Should follow thesis guidelines as specified in the GIMPA thesis handout(Refer to handout for various sections under Chapter 1

· Research focus: Provide one page write up problem statement making sure you consult five different sources

· In stating methodology, there’s the need to cite one of these classical authors; Denzin & Lincoln, Babbie, Yin R., Stake or Creswell, J. W. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, 2003)

· Use of research hypothesis if research methodology is quantitative

· Chapter 2 involves literature review, conceptual & theoretical framework

· Chapter 3 involves Research Design and Methodology

· 3.1 Research Design and Approach – Define the methodology used, cite the sources (minimum 2)

· 3.2 Study population (will look for the information)

· 3.3 Sample size

· 3.4 Sampling Technique (Present evidence about sampling procedure used)

· 3.5 Sources of data

N.B. Write-up will be subjected to turn- it- in application

STUDY AREA (TQMH).docx

STUDY AREA/POPULATION

The Tetteh Quarshie Memorial Hospital, Mampong – Akuapem, serves as a referral health facility for the Akuapem North and South municipalities. The hospital was established in February, 1961 and named after the late Tetteh Quarshie who is credited with the introduction of Cocoa in Ghana in 1879.

According to the Ghana Statistical Service Population and Housing Census of 2010, the Akuapem-North municipality has about 136,483 inhabitants (GSS-PHC, 2010) where Tetteh Quarshie Memorial Hospital’s services are mostly needed. The ridge area consists of people of all walks of life with diverse income levels, diverse educational levels, different ethnic groups, and different social ties.

The hospital has a staff strength of five hundred and forty (540) and provides 24 hour health services. The bed capacity of the facility stands at 150 and is distributed among six units which are accident and emergency, medical, paediatric, palliative, obstetrics and gynaecology and surgical wards with the last two having the theater attached for surgical interventions.

The outpatient department offers services in the areas of antenatal care, general medical care, dental, eye, ear, nose and throat (ENT), x-ray, laboratory, pharmacy, physiotherapy, psychiatric. There is the general administration that oversees the day to day running of the hospital as well as coordinating the activities of the estate, equipment, stores, kitchen, laundry, procurement, maintenance and mortuary units.

MPH thesis supervision Guideline .pdf

GHANA INSTITUTE OF MANAGEMENT AND PUBLIC ADMINISTRATION (GIMPA)

SCHOOL OF PUBLIC SERVICE AND

GOVERNANCE

FACULTY AND STUDENT GUIDELINES FOR COMPLETING THE

MASTER OF PUBLIC HEALTH (MPH) AND MASTER OF PHILOSOPHY (MPHIL) IN PUBLIC HEALTH THESIS

JUNE, 2019

1

Table of Contents

1.0 Introduction ……………………………………………………………… 2

2.0 The Thesis Standards: Scholarly, Rigorous, Generates New Knowledge.. 3

3.0 Types of Acceptable Thesis Projects……………………………………. 3

3.0.1 Some Types of Projects Are Not Acceptable…………………………….. 4

4.0 Standard Thesis Format………………………………………………….... 5

4.1 Title and Preliminary Pages ……………………………………………... 5

4.2 Mandatory Chapters……………………………………………………… 6

5.0 References……………………………………………………………….. 11

6.0 Appendixes……………………………………………………………… 11

7.0 Thesis Supervision………………………………………………………. 12

7.1 Role of Academic Supervisor…………………………………………….. 12

7.2 Responsibilities of the Student…………………………………………… 13

7.3 Supervision Agreement Between Student and Supervisor………………. 13

7.4 Thesis Supervisor Meeting Schedule…………………………………….. 13

8.0 Ethical Approval…………………………………………………………. 14

9.0 Thesis Submission Deadlines……………………………………………. 14

10.0 Thesis Submission Arrangements……………………………………….. 14

11.0 Thesis Assessment………………………………………………………. 15

12.0 Layout Style and Writing Suggestions…………………………………… 15

Appendix 1 Sample: Supervision Agreement Between Student and Supervisor ……... 17

Appendix 2 Sample: Supervision and Verification — Student Record Sheet………… 19

2

1.0. Introduction

These guidelines are intended to serve as a guide to students and faculty for planning, conducting,

and submitting the Master of Public Health (MPH) and the of Master of Philosophy (MPhil) in

public Health master's thesis. The outline presented here incorporates departmental and Graduate

School requirements, and includes discussion of the standards for acceptable theses, the roles and

responsibilities of the supervisor and the student, and detailed guidelines and timeline for

completing the master's thesis.

A thesis is a ‘formal’ document and there are ‘rules’ that govern the way in which it is presented.

It must have chapters that provide an introduction, a literature review, a justification of the data

selected for analysis and research methodology, analysis of the data, discussions and, finally,

conclusions and recommendations.

The Masters level thesis is distinguished from other forms of writing by its attempt to analyze

situations in terms of the ‘bigger picture’. It seeks answers, explanations, makes comparisons and

arrives at generalizations which can be used to extend theory. As well as explaining what can be

done, it addresses the underlying why. The most successful theses are those which are specific and

narrowly focused.

This document is intended to guide you through the thesis process. It can only offer suggestions;

there is nothing that can be said which will guarantee the production of a fine piece of work, but

these are suggestions which, through time, have been found to be both practical and effective.

Thus, this document ultimately aims at is helping make the thesis process predictable,

enlightening, and yes, even enjoyable for both students and faculty.

Students is encouraged to read this guide before starting their thesis and consult it as necessary

throughout the process. This will help make a more effective use of supervision meeting sessions

with the supervisor.

N.B. These notes have been produced for general guidance only and you are required to read the

recommended texts and journal papers on research techniques appropriate to the research methods

3

of your subject discipline. You are not to use these notes as justification or reference for any

methodological approaches or techniques in your thesis.

2.0 The Thesis Standards: Scholarly, Rigorous, Generates New Knowledge

The master's thesis is an original research study that is carried out using rigorous methods that are

appropriate to the research questions, that generates new knowledge, applies concepts and methods

from one or more branches of science relevant to public health, and is presented in a scholarly

format. The thesis demonstrates the student's comprehensive knowledge of the substantive area of

the study and the research methods used. It represents the culmination of the master's program,

and an opportunity to integrate and apply the concepts and methods learned in coursework.

Students in Master of Public Health (MPH) and the of Master of Philosophy (MPhil) in public

Health master's programmes approach the thesis with varied skills in research methods and data

analysis. The thesis is primarily a learning experience for the student, designed to challenge the

student at her/his skill level, while adhering to a standard of high quality regarding the questions

posed, the analytic methods, and the written product.

3.0 Types of Acceptable Thesis Projects

Several different types of projects may fulfill the thesis requirement: case studies, policy analyses,

descriptive studies, analytic studies, program evaluations, or experiments. Each type of study

requires a slightly different approach to formulating research questions, and to collecting and

analyzing data. Regardless of the type of study chosen, the student investigator must apply critical

thought, systematic analysis, and clear presentation.

• Case study: a detailed review of a unique or important program that captures the

background, process, outcomes, successes, failures and lessons learned. The case study

may include either qualitative or quantitative data or both. The case study provides an

opportunity to explore a single program in depth, but places the onus on the investigator to

provide clarity, organization, and scholarship to the investigation. Case studies typically

have limited generalizability.

• Policy analysis: a synthesis of existing and newly collected data brought together in an

organized, structured, and thoughtful manner to answer a policy question or present and

evaluate the strengths and weaknesses of policy options for decision makers. A policy

analysis usually employs multiple sources and types of information (e.g., literature,

4

documents, interviews, secondary data). The policy analysis also places the onus on the

investigator to identify relevant data, and provide clarity, organization, and structure to the

analysis.

• Descriptive study: a qualitative or quantitative study to measure magnitude, variability of

a need or problem, and to explore associated factors. Descriptive studies are often guided

by questions rather than formal hypotheses, and are often the first step in more directed

research.

• Analytic study: a case control or cohort study, although other approaches may fit into this

category. Analytic studies utilize quantitative methods, and are often guided by hypotheses.

Analytic studies typically have clearer methodology than 1. and 2. above, and produce

obvious results. Analytic studies conducted by master's students typically use existing data.

• Program evaluation: structured study to assess whether a program, intervention, or

technique was effective at accomplishing its goals (effectiveness or efficacy for

interventions). A program evaluation addresses explicit questions, and the methods and

measurement may be complex.

• Experiment: a study with randomized or otherwise highly controlled allocation of two or

more identifiable intervention strategies to test a hypothesis, frequently one regarding

causation or treatment effectiveness/ efficacy. The experiment most explicitly addresses

the study question, results are clearly relevant, and can be communicated in a

straightforward way. Feasibility of conducting an experiment is usually limited within the

time frame and resources available to the master's student, given that the student must take

significant initiative in study design and execution.

3.0.1 Some Types of Projects Are Not Acceptable

Some types of projects are not acceptable as theses, including:

• A literature review, though a review with critique and suggestions to the field can be

acceptable. A formal meta-analysis is acceptable in that it generates new knowledge.

• A group project, though the thesis may be part of a collaborative project, provided the

student had the lead role in that part (original work).

• A "warmed over" class or practicum project, though the thesis can be a significant

extension of work that began as a class paper, project, or practicum.

5

4.0 Standard Thesis Format

The Master of Philosophy (MPhil) in Public Health thesis is the culmination of the two-year Master

programme., while the Master of Public Health (MPH) thesis is a culmination of one-year year

full time study. It is a piece of independent writing carried out by Master students under faculty

supervision. It addresses a (set of) research question(s) that are pertinent to public health and the

well-being of mankind. Students are expected to have a solid grasp on their topic of interest,

demonstrate a general command of the relevant literature and the various disciplinary

contributions, as well as an understanding of the concepts and methods used to address the research

question(s).

The standard version of the Master of Philosophy (MPhil) in Public Health thesis requires that

students write a paper of minimum 30, 000 words , while The Master of Public Health (MPH) is

a minimum of 20,000 words, excluding, abstract, footnotes, bibliography and appendices, which

will be awarded 24 credits for the Master of Philosophy (MPhil) in Public Health) and 12 credits

for the Master of Public Health (MPH). The thesis It should present an overview of how relevant

disciplines have addressed a given research question and provide a comprehensive but succinct

analysis of how this research question has been addressed in the literature. Students are encouraged

to undertake original empirical or theoretical analysis on the research question. Thus, the thesis

must not have been previously submitted for examination, award of a degree or publication in the

Institute or elsewhere before submission to GIMPA and thesis shall contain a declaration signed

by the participant that the thesis in question embodies her/his own work. There shall be no joint

authorship of a thesis submitted for a GIMPA MPH or MPhil in Public Health and the Students

shall be invited to defend their thesis.

4.1 Title and Preliminary Pages

The primary pages of the thesis shall include the following:

Title Page:

Latex templates containing correct title pages can be obtained from the School of Public Service

and Governance (SPSG) Secretariat

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Preliminary Pages

• Declaration of originality (see below for the text)

• Dedication (optional)

• Acknowledgements (optional)

• Table of Contents

• List of Tables (if any)

• List of Figures (if any)

• List of Abbreviations and Symbols (if any)

• List of Appendices (if any)

• Abstract

Abstract: should state the problem investigated, scope, aims, outline of the methods used to solve

the problem, and a summary of the results. The abstract should be maximum 250 words.

Declaration of originality: I hereby declare that this thesis was entirely my own work and that

any additional sources of information have been duly cited. I certify that, to the best of my

knowledge, my thesis does not infringe upon anyone’s copyright nor violate any proprietary rights

and that any ideas,

techniques, quotations, or any other material from the work of other people included in my thesis,

published or otherwise, are fully acknowledged in accordance with the standard referencing

practices. Furthermore, to the extent that I have included copyrighted material, I certify that I have

obtained a written permission from the copyright owner(s) to include such material(s) in my thesis

and have included copies of such copyright clearances to my appendix. I declare that this thesis

has not been submitted for a higher degree to any other University or Institution.

4.2 Mandatory Chapters

Standard thesis format includes five sections: Introduction, Literature review, Methods, Results

& Discussion, Summary conclusion & recommendation. Topics within each section are

described below. The organization of subsections may vary, depending on the topic and the

preferences of the student and supervisor

Chapter One: Introduction

This chapter provides an introduction by explaining the context or background of the thesis, a

description of the problem and justification of the problem statement (i.e., why is this a relevant

problem?), the research questions or the hypotheses that will be tested (these should be related to

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the problem), the objectives that you want to reach (these can be subdivided into broad objectives

and more specific objectives) (these need to be related to your research questions or hypotheses),

the research method(s) used to test the hypotheses or to reach your objectives.

Note that the names of the sections may be slightly different depending on the

case.

1.1. Background

1.2. Problem

1.3. Hypotheses/Research Questions

1.4 Objectives

1.3. Justification/Significance/Motivation /Importance

1.6. Structure of the Thesis

Chapter Two: Literature Review

This chapter reviews previous work done in the domain of the thesis. It should start by indicating

what kind of related work has been considered and why. It should end with a conclusion identifying

gaps and remaining problems (which actually justify why the problem is relevant to consider).

The purpose of the study should suggest some theoretical framework to be explained further in

this chapter. The literature review thus describes and analyzes previous research on the topic. This

chapter, however, should not merely string together what other researchers have found. Rather,

you should discuss and analyze the body of knowledge with the ultimate goal of determining what

is known and is not known about the topic. This determination leads to your research questions

The main reasons for the inclusion, in a Master’s thesis, of a literature review section are:

• To present and to analyse, in a critical manner, that part of the published literature which is

relevant to your research topic and which acts as the basis for a fuller understanding of the context

in which you are conducting your research; thus helping the reader to a more rounded appreciation

of what you have completed. Remember critical does not mean looking at the negatives but

forming an evaluation.

• To act as a backdrop against which what you have done in the remainder of the thesis may be

analysed and critically evaluated so as to give the reader the opportunity to assess the worth of

your writing, analytical and research skills.

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• To show that not only have you discovered and reported what you have found to be relevant in

the literature search, but that you have understood it and that you are able to analyse it in a critical

manner.

• To show that your knowledge of the area of interest is detailed enough that you are able to identify

gaps in the coverage of the topic; thus, justifying the reason(s) for your research.

• To show that you know what the key variables, trends and ‘actors’ are in the environment of your

study, i.e. you show that you know what the important issues are that need to be investigated.

• To enable readers to be able to measure the validity of your choice(s) of research methodology,

the appropriateness of the process by which you analyse your results, and whether or not your

findings are congruent with the accepted research which has gone before.

The literature review is presented in the form of a précis, a classification, a comparison and a

critical analysis of that material which is germane to a full understanding of your research study.

Such published material may be drawn rom all, or a combination of, textbooks, journal articles,

conference papers, reports, case studies, the Internet, magazine features or newspaper articles. It

should be remembered, however, that the most important source of academic literature are journal

articles and you should ensure that you are familiar with the most recent publications in journals

relevant to your subject area.

Remember that your literature review should lead and justify the research objectives and questions

of your thesis. Your literature review should not just be a catalogue of authors, frameworks and

ideas but should attempt to introduce a critical evaluation of those authors

work.

Chapter 3: Research Design and Methods

This chapter describes and justifies the data gathering method used. This chapter also outlines how

you analyzed your data. Begin by describing the method you chose and why this method was the

most appropriate. In doing so, you should cite reference literature about the method. Next, detail

every step of the data gathering and analysis process. Although this section varies depending on

method and analysis technique chosen, many of the following areas typically are addressed:

1. Study setting/design

2. Selection of study subjects

i) Source

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ii) Sampling method/recruitment

iii) Criteria for eligibility/exclusion of cases

3. Description of intervention (if any)

4. Data collection

i) Source (e.g., questionnaire, interview, record review, vital records)

ii) Protocol for typical subject

iii) Steps taken to assess and assure data quality

iv) Reliability and validity

5. Analysis plan

i) Hypothesis testing/generation

ii) Definition of key analysis variables

iii) Sample size/power considerations

iv) Statistical methods

6. Ethical considerations

Chapter Four: Results and Discussion.

This chapter presents the evidence and/or results of primary research which you have undertaken

and the discussions thereof. It is preset in two sections. The first section captures the results and

the second section presents the discussion of the results. Depending upon your subject area this

can be in the form of detailed quantitative models, hypothesis testing to some basic analysis using

basic descriptive statistics or qualitative techniques dealing with structured content analysis,

textual analysis, to case study descriptions.

Results

This section of the chapter addresses the results from your data analysis only. This section does

not include It does not include interpretation or discussion of results. Usually you begin by

outlining any descriptive or exploratory/confirmatory analyses (e.g., reliability tests, factor

analysis) that were conducted. You next address the results of the tests of hypotheses. You then

discuss any ex post facto analysis. Tables and/or figures should be used to illustrate and summarize

all numeric information the Table(s) or figure(s) should address each research question. Tables

and figures usually progress from univariate, to bivariate, to multivariate analyses. Text highlights

(but does not duplicate) results shown in tables and figures. For qualitative and historical research,

this chapter usually is organized by the themes or categories uncovered in your research. If you

10

have conducted focus groups or interviews, it is often appropriate to provide a brief descriptive

(e.g., demographic) profile of the participants first. Direct quotation and paraphrasing of data from

focus groups, interviews, or historical artifacts then are used to support the generalizations made.

In some cases, this analysis also includes information from field notes or other interpretative data.

The section should also Provide a clear, systematic presentation of results, linked back to the

research questions and conceptual model.

Discussion

The purpose of this section is not just to reiterate what you found but rather to discuss what your

findings mean in relation to the theoretical body of knowledge on the topic and your profession.

Typically, students skimp on this chapter even though it may be the most important one because

it answers the "So what?" question. In the introduction to the thesis you described the context of

the research. In the literature survey you analyzed the work of previously published authors and

derived a set of questions that needed to be answered to fulfil the objectives of this study. In the

research methodology section, you showed the reader what techniques were available, what their

advantages and disadvantages were, and what guided you to make the choice you did. In the results

section, you present to the reader the outcome of the research exercise. The introduction of this

chapter reminds the reader what, exactly, were the research objectives. Your review of the

literature and your evaluation of the various themes, issues and frameworks helped you to develop

a more specific set of research questions. In essence, your analysis of the data that you have

collected from your fieldwork should provide answers to these questions. You should, as a matter

of priority, focus attention on data that is directly relevant to the research questions. You should

avoid the mistake of including analysis that might be interesting in a general way, but is not linked

to the original direction of the thesis.

The introduction should also explain how the results are to be presented. This is the heart of the

thesis and must be more than descriptive. This chapter develops analytic and critical thinking on

primary results and analysis with reference to theoretical arguments grounded in the literature

review. You should try to highlight where there are major differences and similarities from the

literature or between different groups. Where a model or framework of analysis has been used or

is being developed you should highlight the main relationships as well as explaining the reason

and significance behind features or decisions being discussed.

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The discussion should also contain two mini sections at the tail end that provides the opportunity

to discuss study strengths and the implications of the findings, as follows:

• Study strengths and limitations

• Implications of findings

i) For the theory or conceptual model described in the Introduction.

ii) For public health practitioners or clinicians

iii) For future research

Chapter 5 Summary, Conclusions and Recommendation

A possible (recommended) structure for this chapter is as follows (note that the names of the

sections may be slightly different depending on the case):

5.1. Summary

5.2. Contributions and Conclusion

5.4. Recommendation/Future Work

5.0 References

The Institute has a policy which covers all Masters students in relation to a reference system. It is

important that you get your citations and references correct. You must always cite the source of your

material; inadequate citation could leave you open to the suspicion of plagiarism. The Institute has

approved APA referencing style (6th edition) for use by all students. That means that references in

the text should be given with the name(s) of the author(s) and the years, e.g., (Odonkor et al., 2019)

6.0 Appendixes Contain detailed materials related to the thesis, such as cover letters to respondents, ethical

approval letters, instructions for computing a scale score from the raw data, documentation of the

mathematical equations used in the data analysis, the questionnaires and interview guides used for

the data collection and so forth.

7.0 Thesis Supervision

You will be supported through the thesis by an academic supervisor. You will be advised by your

Programme coordinator of the process by which your programme either allocates academic

supervisors or students seek a preferred academic supervisor. The academic supervisor will ideally

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have background expertise in your area of study. However, this may not always be possible and

you may be allocated a supervisor with more general subject knowledge. Regardless of the subject

background of the supervisor, the academic supervisor will understand the research process.

Where a ‘non-expert’ academic supervisor is appointed you will still, under the guidance of your

academic supervisor, be able to consult with a subject expert. The extent of that subject expert’s

input will usually be limited to advice about the literature review.

7.1Role of Academic Supervisor

The academic supervisor performs many functions and is there to facilitate and not to lead, hence

the responsibility for the quality and content of a thesis is entirely that of yourself, the student. The

supervisor role includes the following:

1. To advise the student whether or not the project appears to be feasible and the possible risks that

may be involved, for example problems in trying to access information, potential poor response

rates to surveys concerning commercially sensitive issues.

2. To assist the student in tailoring the proposal to the time and other resource constraints.

3. To assist the student at the outset in finding useful and relevant reading material and appropriate

academic framework within which to place the topic.

4. To advise on the choice of suitable methodological approach(es).

5. To monitor progress and to advise on what is required to achieve a satisfactory thesis.

6. It should be emphasised that the thesis is entirely your own work. However, you may ask your

supervisor to read in detail a draft of a portion of your thesis normally up to a maximum of two

chapters, in order to give feedback on presentation, content and style. Academic supervisors may

of course pass comment on chapter outlines and may scan quickly through other chapters at their

discretion. The academic supervisor will not check or correct grammar and expression.

7.2 Responsibilities of the Student.

1. To maintain regular contact with the academic supervisor. It is the student's responsibility to

inform their supervisor of progress and to lead the development of the thesis. Difficulties must be

communicated at the time they are encountered. Retrospective information is not acceptable.

2. To write the thesis in a good standard of clear English using appropriate academic terms and

citation and referencing conventions. It is not the responsibility of the supervisor to ensure that

this condition is met.

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3. To write the thesis with guidance from the supervisor. The thesis and research work must be

your own. The thesis is to reflect your subject understanding and research abilities, not that of your

supervisor.

5. To inform the Programme coordinator and academic supervisor of any absence (sickness,

personal, family visits, holidays, work experience) during the time nominated for working on the

thesis. If during the preparation of the thesis, the focus and direction of the thesis

changes substantially from that outlined in your thesis proposal then you

should immediately discuss this with your academic supervisor.

7.3 Supervision Agreement Between Student and Supervisor

There shall be a signed Supervision Agreement Between Student and Supervisor. The agreement

is intended to establish parameters of supervision. It is also to ensure that the supervision

experience is as mutually productive as possible. It also provides clarity in supervisor

responsibilities including the responsibility of the Supervisee (Student). Each and every

supervision meeting must be recorded by the supervisor and signed by the students. See appendix

1 for a guide template for the supervision agreement between student

7.4 Thesis Supervisor Meeting Schedule

Students are allocated up to a maximum of five formal meetings with their academic supervisor

across the duration of the thesis. The purpose of these meetings is to discuss progress and resolve

any difficulties. You will be expected to take a proactive approach to these meetings and bring

material or options to be discussed rather than expect your supervisor to say what should be done

next. Initial meetings to discuss topics and planning will usually be scheduled by the academic

supervisor. Responsibility for scheduling the remaining three meetings will be with you.

Your academic supervisor will endeavour to meet you as soon as possible, but you must remember

your academic supervisor has other work commitments, conferences to attend, research to

undertake and will also take a vacation some time through the long vacation. If you are based

abroad then progress meetings can take place using e-mail.

A record will be kept of each of these meetings detailing the dates of meetings, what was discussed

and any action points. This may be written by the academic supervisor or the student. See appendix

14

2 for a guide template for student – advisor meetings for convenience, although an email record is

also satisfactory.

8.0 Ethical Approval

Regardless of the kind of research a thesis involves, every student proceeding with a thesis is

required to apply for departmental ethical clearance. No thesis will be allowed to proceed or

accepted for examinations/assessment without evidence of ethical approval. Please contact the

MPH programme coordinator for procedures required to obtain n the ethical approval.

9.0 Thesis Submission Deadlines

One of the learning aims of a Master’s programme is to demonstrate the ability to manage a

complex and extended piece of work within the given word count and available timescales. This

requires careful planning and the need to reprioritize and adjust your work as circumstances

change. All students are required to submit their thesis at least three (3) clear months before

graduations or as may announced by the programme coordinator. If a student does not complete

and submit a thesis by the stated time. The thesis will be deferring until the next thesis submission

cycle is opened to allow sufficient time for the student to produce a quality product.

10.0 Thesis Submission Arrangements

Three (3) hard copies of the thesis, written and bound in the approved manner, a copy on a CD-

ROM (using MS Word format), together with the TURNITIN report should be submitted to the

School Secretariat by the published deadline

The thesis deadline will be strictly observed. Thesis s can be submitted earlier. It is vitally

important that students report any problems that have affected or will affect their performance on

the thesis as soon as possible to both the Academic supervisor and the Programme coordinator.

The Examiners can and will take extenuating circumstances into account, but only if they know

about them prior to the Examination Board meeting. All issues relating to a substantive medical

condition causing prolonged incapacity should be supported by a valid medical certificate.

11.0 Thesis Assessment

All thesis s will be read by two examiners, one an internal examiner and the other an external

examiner.

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The following common standards apply.

• The thesis must be presented using a coherent and thoughtful level of English.

• An informed description of events or data is not enough. There must exist an analysis of the

information collected and this must be directed towards answering the research questions raised

by the thesis.

• The thesis must show an awareness of the relevant literature.

• The document should be capable of showing that the author has learnt something new, either

from reviewing the literature or from undertaking an empirical investigation, or both. In addition,

the assessment criteria used in the assessment will reflect the following.

• A depth of knowledge and critical understanding of an interdisciplinary or specialist area that

goes beyond final year undergraduate level and builds upon the student’s postgraduate studies.

• An ability to bring together in a coherent fashion the perspectives of two or more theoretical

standpoints and apply the results in a practical setting.

• An ability to appreciate critically, to a higher level than that of a final year undergraduate student,

major issues and problems internal to the discipline and/or with regard to the discipline’s impact

on the external world.

12. Layout Style and Writing Suggestions Aspect ‘Word’

Command Line Recommendation setting

The standard version of the Master of Philosophy (MPhil) in Public Health thesis requires that students write a paper of minimum 30, 000 words , while The Master of Public Health (MPH) is a minimum of 20,000 words ( excluding, abstract, footnotes, bibliography and appendices)

Paper size File, page set-up- paper A4 Margin setting File, page set-up- margin Top 2.54cm Bottom 2.54cm

Left 3.17cm Right 3.17cm Gutter 0cm Gutter Position Left

Line spacing Format, Paragraph, Indent & spacing

double spacing

Page Numbering View For sections from Acknowledgements to start of Main Text page numbers format i) ii)

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Header & footer, insert, page numbers

iii) …. And so, on continuously with position on page centred aligned

Text Format, Style& Formatting Times New Roman Font Format, Style& Formatting 12-point font Alignment Format, Style & Formatting Justified Typical Dissertations layout

Front cover

Required, see separate example

Acknowledgements Required, Abstract

Required

Table of Content Index, Reference, Index & Tables

Required

List of Figures Index, Reference, Index & Tables

Required (Note all figures in the main text must be numbered, titled and attributed)

List of Tables Index, Reference, Index & Tables

Required (Note all tables in the main text must be numbered, titled and attributed)

Abbreviation Optional Main text Chapter & Section

Format, Bullets & Numbering,

Chapter title, bold, outlined numbered e.g. chapter 1, start each chapter on new page

Headings Outline numbered Section title, bold, out lined numbered e.g. 1.1, Sub section title, outlined numbered e.g. 1.1.1. Font size for headings should be APA New Times Roman 14

List of references APA System required (6th Edition) Appendices Appendix title, bold outlined numbered e.g.

Appendix 1, start each Appendix on new page

Binding Number of Copies

Spiral soft bound, (not a A4 Ring Binder) Three hard bound copies plus an electronic version, Word or pdf format. Plus, plagiarism report from Turnitin

17

Appendix 1:

Sample: Supervision Agreement Between Student and Supervisor

GIMPA

SCHOOL OF PUBLIC SERVICES AND GOVERNANCE

SUPERVISION AGREEMENT BETWEEN STUDENT AND SUPERVISOR

FOR THE: MASTER OF PUBLIC HEALTH/ MASTER OF PHILOSOPHY IN

PUBLIC HEALTH PROGRAMMES

This document is intended to establish parameters of supervision. It is designed to ensure that the supervision experience is as mutually productive as possible. It also provides clarity in supervisor responsibilities including the responsibility of the Supervisee (Student). This supervision agreement between DR. STEPHEN T. ODONKOR (the supervisor) of the Ghana Institute Management and Public Administration (GIMPA) and the candidate/supervisee whose details are as follows: Full Name: ____________________________________________________________________

Student Number: _______________________________________________________________

Degree Registered for: ___________________________________________________________

Telephone: _______________________________Email: _______________________________

B. Duties and Responsibilities of the Supervisee (Student) 1. Follow instructions 2. Improve and correct mistakes 3. Rewrite and correct any shortcomings 4. Present final form of thesis before thesis is presented for examination 5. Conduct research independently 6. Master the terminology of the subject 7. Formulate ideas 8. Cover and integrate the literature on a specific subject and draw own conclusions 9. Point out significance of the research and implications of findings 10. Provides feedback weekly to supervisor on supervision process 11. Respond non-defensively to supervisor feedback 12. Consults with supervisor in all cases of emergency 13. Implements supervisor directives in subsequent sessions or before as indicated. C. Duties and Responsibilities of the Supervisor 1. Give student proper guidance (Compilation and planning of project work) 2. Merely verify the sources in broad outline 3. Draw attention to poorly formulated statements

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4. Merely point out linguistic errors 5. Never rewrite parts of the student’s work 6. Give general criticism and comments on technical factors and methodology 7. Ensure that thesis/ project work contains prescribed summary and keywords 8. Liaise continually with co-supervisor, if any, about the student’s work 9. Compilation of reading list if formal study programme is prescribed 10. Primary evaluator of the work: advise on the academic standard of the thesis/ project work 11. Monitor progress and make recommendation or refusal to the School/Institute. 12. Be available to write letters of reference for the student and submit them in a timely fashion after completion of degree D. Procedural Aspects

1. Maintain intensive interaction throughout the defined period. 2. Meet on a regular (electronic means and face to face or both) basis to set both short- and

long-term goals 3. Progress reports will be submitted to the School/Institute describing your development,

strengths, and areas of concern. 4. If the supervisor or the supervisee must cancel or miss a supervision session, the session

will be rescheduled. 5. The supervisee may contact the supervisor on (contact #) 0243709702 or by email on

[email protected] or [email protected]. 6. The supervisor must be contacted for all emergency situations.

E. Duration of Agreement The agreement is valid for six months (6) from the date of signing the contract. If the supervisee (student) fails to complete his or her thesis/Project work within this time, the agreement will be deemed to have been cancelled. If the supervisor agrees to supervise the student again upon the expiry of the agreement then a new agreement will have to be signed, terms and conditions will then apply. F. Financial Obligations The student has NO financial obligation under this agreement. H. Signature We ______________________________________________________________(supervisee) and Dr. Stephen T. Odonkor (supervisor) agree to follow the directives laid out in this supervision agreement and to conduct ourselves in keeping with our Ethical Principles and Code of Conduct. Supervisee’s Signature: ______________________________Date: _______________________

Supervisor’s Signature: ______________________________Date: _____________________

Agreement Start Date: ___________________________________________________________

Agreement Expiry Date: _________________________________________________________ Once signed by all parties, copies must retain by the candidate and supervisor.

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Appendix 2 Sample: Supervision and Verification — Student Record Sheet

(A minimum number of formal meetings between research students and their supervisors is recommend: at least 10 times for MPhil In Public Health students and at least5 times MPH for students. For each of these sessions a Supervision Record must be completed.)

• All work that students submit for school assessment and external assessment must be their own, produced without

undue assistance from other people or sources. • For school assessments, teachers and students may use, or adapt, this record sheet. If used, these sheets are to

be kept at the school until the end of the completion of the thesis. • For external assessments that involve an investigation process, teachers and students must use this record sheet

to record and authenticate each student’s work.

Thesis title: __________________________________ _______________________________________________

Name of student _______________________________________ Registration______________________

Name of supervisor ____________________________ Assessment task ________________________________

Examples of stages of development Supervisor initials Student initials Date Comments

Preparation and planning Student has: • decided on the scope of the task, which is consistent with the

requirements of the subject outline Student has identified, as appropriate: • possible focus questions, context, and/or outcomes • resources and data • the skills, activities, investigation/research methods, and/or

processes required • the mode of presentation.

Student has communicated progress of work to the teacher Development as appropriate Student has: • developed and gathered notes, appendices, and/or references • conducted any surveys, experiments, or other research • validated sources of information • analysed and/or evaluated findings and/or results • explained information from source material in their own words • acknowledged all information and ideas that are not their own • kept any quoted material to a minimum • drafted the report and/or presentation.

Student has discussed progress and/or results with the teacher Draft presentation Student has presented for feedback a draft that: • meets the requirements of the subject outline (e.g. word count) • includes all relevant support material and references • Student has undertaken any revisions as appropriate. Only one completed draft should be presented for feedback.

Final presentation • Student has presented the final piece of work.

Signature of student Date

Signature of Supervisor Date

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Sample__ examination question_-1.doc

GHANA INSTITUTE OF MANAGEMENT AND PUBLIC ADMINISTRATION (GIMPA)

image1

2018 END OF SEMESTER EXAMINATION

TITLE OF PAPER: RESEARCH METHODS (SPSG 704B)

LEVEL: MSc, Occupational Health, Safety & Environmental Management (MOSHEM)

DATE: 21st May 2020

EXAMINER: NAPOLEON KURANTIN PhD.

TIME ALLOWED: THREE (3) HOURS

Instruction

Answer only three questions. Your answers must be based on all required readings and class discussions. The end of term examinations counts for fifty (50) percent of the final grade.

Note: Question 2(two) is compulsory.

Question 1: (16.66% Marks)

What is the difference between qualitative and quantitative research? Include mention of possible differences in purpose, method, data sources, and data analysis.

Question 2: (16.66% Marks)

The Ministry of Health and Environment in your home country consulted you to determine the mean heart rate after a particular type of COVID-19 patient admitted to the National Infectious Disease Control Unit was so different from the larger healthy population rate of 72 beats per minute. You are to consider a mean difference of 6 beats per minute to be clinically meaningful. You decided to apply 9.1 beats per minute as the variation based on a previously published study. In your estimation, how many COVID-19 cases will be needed to carry out the study at 5% alpha and 80% power? Use the equation below as a guide.

image4.jpg

Question 3: (16.66% Marks)

Provide a detailed explanation of the following sample techniques in qualitative paradigm:

A. Heterogenous

B. Convivence

C. Saturation

D. Deviant

E. Theoretical

Question 4: (16.66% Marks)

You are charged with designing the methodology for a policy research proposal. Provide an annotated outline for a quantitative and qualitative study respectively.

Question 5: (16.66 %Marks)

What is the main purpose of mixed methods research? As part of your write up, provide five core characteristics of mixed methods research.

Question 6A: (8.33% Marks)

You as a teacher assigns trigonometry practice problems to be worked via the net. Students must use a password to access the problems and the time of log-in and log-off are automatically recorded for the teacher. At the end of the week, you examined the amount of time each student spent working the assigned problems. The data is provided below in minutes:

15 28 25 48 22 43 49 34 22 33 27 25 22 20 39

· Find the Mean, Median, and Mode for the above data.

· What does this information tell you about students' length of time on the computer solving trigonometry problems?

· Is this data skewed?

Question 6B: (8.33% Marks)

As an environmental and quality assurance officer responsible for the airing of quality television programmes, you have been tasked to an assessment of a new talk show on Ghana Broadcasting Corporation (GBC). For two weeks, you decided to count the number of words that must be "bleeped" as too obscene for television and the number of physical altercations. You hope that after recording this data that you will be able to argue that the show is inappropriate for television particularly during the day. The data for number of words censored is provided below:

342, 267, 321, 157, 33, 349, 254, 166, 132, 289

· Find the Mean, Median, and Mode for the above data.

· What does this information tell you about the talk show?

· Is this data skewed?

Good Luck!image2.png

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