Discussion and Data analysis Chapter-2000w

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WEEKS 2 & 3: DATA ANALYSIS AND DISCUSSION OF THE RESULTS

Topic goals

 Understand content analysis.

 Present some of the theoretical models on data analysis and choose the most appropriate

for your research.

 To help you understand the stages involved in qualitative and quantitative data analysis.

 Interpretation of the data in order to get the final conclusions.

 Discussion of the final results

Tasks

 Discussion Forum 1: Discuss the importance of choosing the correct method (quantitative,

qualitative or mixed method)

 Discussion Forum 2: Discuss with your colleagues your experience while writing the

chapters on "Discussion and data analysis".

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INTRODUCTION TO QUALITATIVE DATA ANALYSIS

In previous weeks you are probably had learnt of the basic differences between qualitative and

quantitative research methods and it hoped that you are now familiar with these differences and

the different applications those methods.

Qualitative data analysis

Qualitative Data Analysis (QDA) can be looked at as the range of processes and procedures that

involves the movement from the data collection into some form of explanation, understanding or

interpretation of the people and situations we are investigating.

According to Pope, Ziebland & Mays (2000: 114):

“Qualitative research uses analytical categories to describe and

explain social phenomena. These categories may be derived

inductively or used deductively, either at the beginning or part way

through the analysis as a way of approaching the data”.

It is important to indicate that Qualitative Data Analysis is usually based on an interpretative

philosophy. The idea of this philosophy is that it focuses on the examination the meaningful and

symbolic context of qualitative data.

It should be noted that during the interviews a generous amount of words is usually created by and

this data needs to be described and summarised.

The qualitative researcher may seek relationships between various themes that have been

identified, or to relate behaviour or ideas to biographical characteristics of respondents such as age

or gender from the questions that have been asked.

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Approaches in Analysis

a) Deductive approach

This approach is used when the researcher is using his or her research questions to group the data

and then look for similarities and differences.

 This approach is used when time and resources are limited

 It is also used when the qualitative research is just a smaller component of a larger

quantitative study.

Deductive content analysis is often used in cases where the researcher wishes to retest existing

data in a new context. This may involve testing categories, concepts, models or hypotheses. If

the researcher has chosen the deductive approach, he/she has to develop a categorization matrix

and to code the data according to the categories. The deductive content analysis is based on

earlier work such as theories, models, mind maps and literature reviews.

After a categorization matrix has been developed, all the data are reviewed for content and

coded for correspondence with or exemplification of the identified categories.

Elo & Kyngas, 2008:111.

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b) Inductive approach

 This approach is used when qualitative research is seen to be a major research approach of

the inquiry.

 This approach involves using emergent framework to group the data and then look for

relationships

If the inductive method is chosen by the researcher, the next step is to organize the qualitative data.

During this process the research focuses on creating categories, open coding and abstraction (Elo

& Kyngas, 2008).

Open coding: notes and headings are written in the text while reading it. The headings –as many

as necessary- are written down in the margins to describe all aspects of the content. The headings

are collected from the margins on to coding sheets and categories are freely generated at this stage.

Categories: after the open coding, the categories are grouped under higher order headings. The

purpose of creating categories is to provide a means of describing the phenomenon, to increase

understanding and to generate knowledge.

Abstraction: is the formulation of a general description of the research topic through generating

categories. Each category is named using content characteristic words. Subcategories with similar

events and incidents are grouped together as categories and categories are grouped as main

categories. The abstraction process continues as far as is reasonable and possible (Elo & Kyngas,

2008).

The data analysis process in qualitative research

i. Familiarisation with the data through review, reading, listening etc

ii. Transcription of tape recorded material

iii. Organisation and indexing of data for easy retrieval and identification

iv. Anonymising of sensitive data

v. Coding (may be called indexing)

vi. Identification of themes

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vii. Re-coding

viii. Development of provisional categories

ix. Exploration of relationships between categories

x. Refinement of themes and categories

xi. Development of theory and incorporation of pre-existing knowledge

xii. Testing of theory against the data

xiii. Report writing, including excerpts from original data if appropriate (e.g. quotes from

interviews)

Adapted from Lacey and Luff (2009, p. 6-7)

What do you want to get out of your data?

Let’s take an example based on the research question provided below about the psychological

needs of the teen mothers:

Research question:

i. What are the perceptions of teen mothers, as regards their own psychological needs?

ii. You may be interested in finding out the psychological services that needs to be provided

in order the perceived needs of the teen mothers to be met.

iii. You might also be interested to know what kind of psychological services are needed or

Types of qualitative data analysis

i. Content Data Analysis

Content Data Analysis simply involves the process of counting the number of times a particular

word or concept occurs (e.g. loneliness) in a narrative.

It is a method of analysing written, verbal or visual communication messages. Content analysis is

commonly used in communication, journalism, sociology, psychology and business and during the

last few decades its use has shown steady growth (Neundorf, 2002).

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As a research method, is a systematic and objective means of describing and quantifying

phenomena. It is also known as a method of analysing documents. Content analysis allows the

researcher to test theoretical issues to augment understanding of the data. Through the analysis, it

is possible to filter words into fewer content-related categories. It is assumed that when classified

into the same categories, words, phrases and the resembling share the same meaning.

Content analysis is a research method for making replicable and valid interpretations from data to

their context, with the purpose of providing knowledge, new visions, a representation of facts and

a practical guide to action. The aim is to attain a condensed and broad description of the

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phenomenon, and the outcome of the analysis is concepts or categories describing the

phenomenon.

ii. Thematic Data Analysis

In this approach all units of data (e.g sentences or paragraphs) are given a particular code, extracted

and examined in more detail. Do participants talk of being lonely even when others are present?

Are there particular times of day or week when they experience loneliness? In what terms do they

express loneliness? Are those who speak of loneliness are also those who experience depress?

Such questions can lead to themes which could eventually be developed such as ‘lonely but never

alone’.

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Thematic Analysis approach is a method for identifying, analysing, and reporting patterns (themes)

within data. It minimally organises and describes your data set in (rich) detail. However, it also

often goes further than this, and interprets various aspects of the research topic (Boyatzis, 1998).

Boyatzis (1998) defines the 'unit of coding' as the most basic segment or element of the raw data

of information that can be assessed in a meaningful way regarding the phenomenon (pxi)

A good thematic code 'captures the qualitative richness of the phenomenon' (Boyatzis 1998, p31)

and has 5 elements:

i. A label

ii. A definition of when the theme occurs

iii. A description of how to know when the theme occurs

iv. A description of any qualifications or exclusions to the theme

v. Examples to eliminate possible confusion when looking at the theme

Phases of thematic analysis (inductive and deductive)

According to Braun and Clarke, (2006) the phase Description of the Process in thematic data

analysis are as follows:

i. Development of a priori codes: Determining important theoretical areas that can be used

as initial codes to organize the data (Boyatzis, 1998). Use of theory-driven coding that

links to the theoretical framework of the study.

ii. 2. Familiarization with the data Transcription of data and field notes, reading and re-

reading the data, noting down initial ideas (Braun and Clarke, 2006)

iii. Carrying out theory-driven coding. Coding data in a systematic fashion within each

interview and the field notes and across the entire data collating data relevant to each a

priori code (Boyatzis 1998; Braun and Clarke, 2006)

iv. Reviewing and revising codes and Carrying out additional data-driven coding Reviewing

and revising theory-driven codes in the context of the data (Boyatzis, 1998). Additional

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coding is done at this stage, which is not confined by the a priori codes and inductive (data-

driven) codes are assigned to the data (Fereday and Muir-Cochrane, 2006).

v. Searching for themes Collating codes into potential themes, gathering all data relevant to

each potential theme (Braun and Clarke, 2006; Fereday and Muir-Cochrane, 2006)

vi. Reviewing themes Checking if the themes produced are related to the coded extracts

(Level 1) and the entire data set (Level 2) as well as developing the thematic ‘map’ of the

analysis (Braun and Clarke, 2006) so as to determine credibility of the themes (Fereday

and Muir-Cochrane, 2006).

vii. Producing the report The final opportunity for the analysis in which vivid compelling

extract examples are selected, final analysis of selected extracts, relating back the analysis

to the research questions and the relevant literature and producing a scholarly report of the

analysis (Braun and Clarke, 2006

iii. Grounded Theory

It was developed out of research by sociologists Glaser and Strauss (1967). Glaser and Strauss

were concerned to outline an inductive method of qualitative research which would allow social

theory to be generated systematically from data. As such theories should be ‘grounded’ in rigorous

empirical research, rather than to be produced based in the abstract.

i. Grounded theory is a methodology; it is a way of thinking about and conceptualising data.

It is an approach to research as a whole and as such can use a range of different methods.

ii. Grounded Theory analysis is inductive, in that the resulting theory ‘emerges’ from the data

through a process of rigorous and structured analysis.

The Procedure and Rules of the Grounded Theory approach

i. Data Collection and Analysis are Interrelated Processes. In grounded theory, the analysis

begins as soon as the first bit of data is collected.

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ii. Concepts Are the Basic Units of Analysis. A theorist works with conceptualizations of data,

not the actual data per se. Theories can't be built with actual incidents or activities as observed

or reported; that is, from "raw data." The incidents, events, and happenings are taken as, or

analyzed as, potential indicators of phenomena, which are thereby given conceptual labels. If

a respondent says to the researcher, "Each day I spread my activities over the morning, resting

between shaving and bathing," then the researcher might label this phenomenon as "pacing."

As the researcher encounters other incidents, and when after comparison to the first, they

appear to resemble the same phenomena, then these, too, can be labeled as "pacing." Only by

comparing incidents and naming like phenomena with the same term can a theorist accumulate

the basic units for theory. In the grounded theory approach such concepts become more

numerous and more abstract as the analysis continues

iii. Categories Must Be Developed and Related. Concepts that pertain to the same phenomenon

may be grouped to form categories. Not all concepts become categories. Categories are higher

in level and more abstract than the concepts they represent. They are generated through the

same analytic process of making comparisons to highlight similarities and differences that is

used to produce lower level concepts. Categories are the "cornerstones" of a developing theory.

They provide the means by which a theory can be integrated.

iv. Sampling in Grounded Theory Proceeds on Theoretical Grounds. Sampling proceeds not in

terms of drawing samples of specific groups of individuals, units of time, and so on, but in

terms of concepts, their properties, dimensions, and variations.

v. Analysis Makes Use of Constant Comparisons. As an incident is noted, it should be compared

against other incidents for similarities and differences. The resulting concepts are labeled as

such, and over time, they are compared and grouped as previously described.

vi. Patterns and Variations Must Be Accounted For. The data must be examined for regularity and

for an understanding of where that regularity is not apparent.

vii. Process Must Be Built Into the Theory. In grounded theory, process has several meanings.

Process analysis can mean breaking a phenomenon down into stages, phases, or steps. Process

may also denote purposeful action/interaction that is not necessarily progressive, but changes

in response to prevailing conditions

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viii. Writing Theoretical Memos Is an Integral Part of Doing Grounded Theory. Since the

analyst cannot readily keep track of all the categories, properties, hypotheses, and generative

questions that evolve from the analytical process, there must be a system for doing so. The use

of memos constitutes such a system. Memos are not simply about "ideas."

(adapted from Corbin and Strauss, 1990, pp.7-10)

Potential pitfalls to be avoided in qualitative analysis

Braun and Clarke (2006 pp 94-95) identify some "potential pitfalls" to be avoided in qualitative

analysis

i. A failure to actually analyse the data

ii. Using data collection questions as themes that are reported

iii. A weak or unconvincing analysis

iv. A mismatch between the data and the analytic claims that are made about it. 1.

Example of qualitative data analysis using thematic analysis

Question: “how do you feel about your student accommodation?”

Participants: 10 Master’s students living in student accommodation an open question

• You have coded three data segments using the code ‘satisfactory accommodation’. You have

defined ‘satisfactory’ as instances when students indicate that their accommodation generally

meets their needs, but they report mixed views, balancing positive opinions with critical

comments. You have decided not to include views which are almost exclusively positive or

negative. The data segments you have coded as ‘satisfactory’ are:

‘It’s okay – it’s not my home, my house at home in my country, but I have the things I need, desk,

bed, arm chair, clean and warm, not damp or anything.’ (Student 3)

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‘It could be nicer – the decoration is a bit old, and it can be a little bit noisy at night sometimes –

but overall it’s fine just for students. When I graduate and get a job, I want to rent a more modern

apartment, fashionable with lots of technology.’ (Student 9)

‘The only thing is it’s a bit small… I can’t invite all my friends to my room to watch television or

chat, so we have to go to the coffee shop, cinema… it’s a bit expensive always going out. That’s

the main problem, but I quite like it, it’s quite good, I feel quite safe.’ (Student 2)

Is it okay to say ‘3 students reported that their accommodation was satisfactory’?

In qualitative studies, we are interested in individual’s feelings, thoughts, beliefs and unique

contributions. It is ok to say that 3 students reported that about their accommodation.

Quantitative data analysis

Quantitative data analysis provides quantifiable, objective, and easy to interpret results. The data

can typically be summarized in a way that allows for generalizations that can be applied to the

greater population and the results can be reproduced. The design of most quantitative studies also

helps to ensure that personal bias does not impact the data. Quantitative data can be analyzed in

several ways. The most commonly used quantitative analysis procedures are described in the

following section.

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The first step in quantitative data analysis is to identify the levels or scales of measurement as

nominal, ordinal, interval or ratio. This is an important first step because it will help you determine

how best to organize the data. The data can typically be entered into a spreadsheet and organized

or “coded” in some way that begins to give meaning to the data.

The next step would be to use descriptive statistics to summarize or “describe” the data. It can be

difficult to identify patterns or visualize what the data is showing if you are just looking at raw

data.

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Following is a list of commonly used descriptive statistics:

Frequencies

A count of the number of times a particular score or value

is found in the data set

Percentages

Used to express a set of scores or values as a percentage of

the whole

Mean Numerical average of the scores or values for a particular

variable

Median The numerical midpoint of the scores or values that is at the

center of the distribution of the scores

Mode The most common score or value for a particular variable

Minimum

maximum

(range)

and

values

The highest and lowest values or scores for any variable

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It is now apparent why determining the scale of measurement is important before beginning to

utilize descriptive statistics. For example, nominal scales where data is coded, as in the case of

gender, would not have a mean score. Therefore, you must first use the scale of measurement to

determine what type of descriptive statistic may be appropriate. The results are then expressed as

exact numbers and allow you to begin to give meaning to the data.

For some studies, descriptive statistics may be sufficient if you do not need to generalize the results

to a larger population. For example, if you are comparing the percentage of teenagers that smoke

in private versus public high schools, descriptive statistics may be sufficient.

However, if you want to utilize the data to make interpretations or predictions about the population,

you will need to go a step farther and use inferential statistics. Inferential statistics examine the

differences and relationships between two or more samples of the population. These are more

complex analyses and are looking for significant differences between variables and the sample

groups of the population. Inferential statistics allow you test hypotheses and generalize results to

population as whole.

Following is a list of basic inferential statistical tests:

Correlation: seeks to describe the nature of a relationship between two variables, such as strong,

negative positive, weak, or statistically significant. If a correlation is found, it usually indicates a

relationship or pattern.

Analysis of Variance (ANOVA): tries to determine whether or not the means of two sampled

groups is statistically significant or due to random chance. For example, the test scores of two

groups of students are examined and proven to be significantly different. The ANOVA will tell

you if the difference is significant, but it does not speculate regarding “why”.

Regression: used to determine whether one variable is a predictor of another variable. For

example, a regression analysis may indicate to you whether or not participating in a test preparation

program results in higher ACT scores for high school students.

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Finally, the type of data analysis will also depend on the number of variables in the study. Studies

may be univariate, bivariate or multivariate in nature.

There are many statistical software programs in analyzing quantitative data:

SPSS – The Statistical Package for Social Science (SPSS) is one of the most commonly used

software packages in social science research. One advantage is that is co mprehensive and

compatible with nearly any type of data file. SPSS is very user-friendly and can be used to run

both descriptive statistics and other more complicated analyses. Data can be entered directly into

the program will also generate reports, graphs, plots, and trend lines based on the data analyses.

STATA – This is an interactive program that can also be used for both simple and complex

analyses. It will also generate charts, graphs and plots of your data and results. This program may

seem a bit more complicated to some researchers. It uses four different windows including the

command window, the review window, the result window and the variable window. While it is a

very useful program, the organization of this software may seem discouraging.

SAS – The Statistical Analysis System (SAS) is another great statistical software package that can

work with very large data sets. It has additional capabilities that make it commonly used in the

business world because it can address issues such as business forecasting, quality improvement,

planning, and so forth. It is a great program for data sets that need to incorporate strata, weighting,

or groups. However, some knowledge of programming language is required to operate the

software, making it a less appealing option for some.

R programming: R is an open source programming language and software environment for

statistical computing and graphics that is supported by the R Foundation for Statistical Computing.

The R language is commonly used among statisticians and data miners for developing statistical

software and data analysis (Blaikie, 2003).

Once finishing the data analysis, the way to present the data is very significant. Some key points

are converting data analysis software outputs into readable tables, using charts and graphs and

drowning in data.

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It is important to get consent before starting the research process. Ethical issues are important in

all types of research. Regardless of the type of research, the researcher should take into

consideration both general research principles and those that are more specific to the type of

research. In quantitative research, ethical standards prevent against such things as the fabrication

or falsifying of data and therefore, promote the pursuit of knowledge and truth which is the primary

goal of research. It is also important to protect research participants and follow the guiding

foundation of “do no harm” if human subjects are utilized in the study.

Discussion of the results

After the data analysis comes the discussion of the results/findings.

This section has four purposes, it should:

• Interpret and explain your results

• Answer your research question

• Justify your approach

• Critically evaluate your study

The discussion section therefore needs to review your findings in the context of the literature and

the existing knowledge about the subject. The discussion provides an interpretation of the findings

of the dissertation research within the context of the literature review, using key citations from the

review of the literature.

You also need to demonstrate that you understand the limitations of your research and the

implications of your findings for policy and practice. This section should be written in the present

tense.

As mentioned above, in this chapter you should refer to the hypotheses, objectives, or questions.

Assess the meaning of the results by evaluating and interpreting. Speculation should be reasonable,

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firmly justified, and subject to test. List the primary research questions from the methodology

chapter and answer them with the results.

You should discuss the meaning of the results here, in brief, and highlight any important areas that

you have identified. You should also look at the different things that the study means and how this

is evaluated to the overall understanding in your dissertation.

In this part you should also mention any gaps or weaknesses in the area of your research, provide

supporting evidence based on the work of other authors and give new and innovative ideas for

improvement. It’s important to state the impact of your research. You can present your data and

discuss the findings with graphs and/or tables so as to illustrate your points.

Every result included and discussed must have a method set out in the methods section. Check

back to make sure that you have included all the relevant methods.

Conversely, every method should also have some results given so, if you choose to exclude certain

experiments from the results, make sure that you remove mention of the method as well.

Kindly note that during the writing of your dissertation it’s preferable to provide books or journal

articles from valid sources and were published in the last five years.

Structure of writing the Discussion

 You need to make a summary of the purpose of the study and restate the research questions

 You also need to make a summary of your research methods

Major Findings (then you need to use as many headings/subheadings as necessary)

 Describe, interpret and evaluate major findings of the research

 For any surprising findings, indicate possible reasons for the result

 Reasons may have to do with all aspects of your design, procedure, nature of

participants/respondents, and the nature of the selected measurements.

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As you prepare your discussion there is need to organize your discussion using a logical outline

that fits the way you organized your introduction, specifically in relation to each hypothesis. This

therefore means that for each main finding, indicate the possible reasons why you did or did not

find the pattern in your data that you had hypothesized or expected. Assess the meaning of your

results through evaluation and interpretation.

There is need for you to compare each main finding with the trends you found in the literature. If

your finding is different, explain what may account for that difference (e.g., between the method

and results of your study and those of another).

Producing the report of the data

Several students suggested their accommodation, while having some limitations, was generally

satisfactory, being ‘okay’ (student 2) or ‘fine for students’ (student 9). Their accommodation

appeared to meet many of their needs, for instance, student 3 commented ‘I have the things I need,

a desk, bed, arm chair, clean and warm, not damp or anything’, while student 2 reported she ‘feels

quite safe’. However, they also noted some limitations, for example, about the limited space: ‘it’s

a bit small… I can’t invite all my friends to my room’ (student 2), and the décor: ‘it could be nicer

– the decoration is a bit old’ (student 9). Nonetheless, the students seemed to be quite accepting of

these limitations – notably, student 2 still said ‘I quite like it, it’s quite good’ even though she

found it quite expensive going out to see friends because her room was too small to invite them

over.

There was also some suggestion that the students tended to think of their accommodation as

temporary; student 3 is clear ‘it is not my home, my house’, while student 9 is already planning to

rent a more modern apartment which suits his tastes better on graduating. This might be considered

to have made them more accepting of their accommodation’s limitations, as long as their

accommodation generally meets their main needs as students.

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Summary:

i. The words in bold and underlined fond indicate how we suggest possible conclusions from

the data as in qualitative research we talk about interpretations and how ‘reality’ is

constructed by other people’s point of view.

ii. Therefore we tend not to say that e.g. ‘students are not satisfied’ we prefer to report

‘students seem not to be satisfied’

This chapter presents the evidence and/or results of primary research which you have undertaken.

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. The main part

of the chapter is the presentation of the data that you obtained. Even projects of relatively moderate

dimensions will generate a large amount of data which has to be considered.

This data must be organised in a logical and coherently ordered whole so that your thought

processes and interpretation are clear to the reader. Whatever form of data analysis has been

undertaken; it must be accomplished with care and attention to detail, as should the way in which

the results are presented. Nothing is guaranteed to frustrate a reader more than to have to plough

their way through an arid mass of tables, figures and statistics. Better by far to describe in an

accessible manner (which does not mean that you should talk down to the reader) what the research

has uncovered and to include only the most pertinent figures as evidence of your findings.

Dissertations which included detailed modeling or quantitative analysis will clearly need to show

all relevant assumptions, relationships and methods. Your academic supervisor will be able to

advise on the level of detail required in the main body as opposed to that included in the Appendix.

Graphs, diagrams, pie-charts etc. are all useful ways of presenting research results; they are an

imaginative way of ‘breaking up’ solid blocks of text – they let a little ‘light’ into the body of the

text as long as they are relevant and illustrate your points.

Keep your review to those items which are relevant to your research question and not just

everything I found out. There will be problems in the execution of any research project and their

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occurrence should be brought to the attention of the reader. Without stating them, one of the

essential elements of the context in which the research took place will be missing.

Not all dissertations contain quantitative data. In many situations, students will have made

extensive use of qualitative research techniques such as focus groups and/or in-depth unstructured

interviews. While quantitative data lends itself to graphs, tables and so on, qualitative data, and

the way it is presented, pose particular challenges for students. As ever, your objective should be

based on the belief that the data must be presented in such a manner as to make it easy for the

reader to follow the logic of the analysis.

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Reference list

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https://cirt.gcu.edu/research/developmentresources/research_ready/quantresearc

h/analyze_data.

Blaikie, N. (2003). Analyzing quantitative data: From description to explanation. Sage.

Boyatzis, R. E. (1998). Transforming qualitative information: Thematic analysis and code

development. sage.

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative research in

psychology, 3(2), 77-101.

Corbin, J. M., & Strauss, A. (1990). Grounded theory research: Procedures, canons, and evaluative

criteria. Qualitative sociology, 13(1), 3-21.

Elo S. & Kyngas H. (2008) The qualitative content analysis process. Journal of Advanced Nursing

62(1), 107–115. doi: 10.1111/j.1365-2648.2007.04569.x

Fereday, J. and Muir-Cochrane, E., (2006). Demonstrating rigour using thematic analysis: A

hybrid approach of inductive and deductive coding and theme development. International

journal of qualitative methods, 5(1), pp.80-92.

Glaser, B., & Strauss, A. (1967). The discovery of grounded theory. Weidenfield & Nicolson,

London, 1-19.

Lacey A. and Luff D. (2009) Qualitative Research Analysis. The NIHR RDS for the East Midlands

/ Yorkshire & the Humber.

Lewins, A.; Taylor, C. and Gibbs, G. (2010) What is Qualitative Data Analysis (QDA)? Retrieved

from http://onlineqda.hud.ac.uk/Intro_QDA/what_is_qda.php.

Neundorf, K. (2002) The Content Analysis Guidebook. Sage Publications Inc., Thousand Oaks,

CA.

Pope, C., Ziebland, S. & Mays, N. (2000). Qualitative research in health care: Analysing

qualitative data. In Education and debate, pp. 114-116.