Statistical - Write up of Results

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DATASETS-problem23.docx

PROBLEMS/DATA SETS

The following problems (with associated data sets) are designed to test your ability to determine the proper multivariate statistical method(s) to apply in order to answer the research question(s) of interest.

For each problem, submit a document that describes:

(1) the research question(s) of interest

(2) the method of analysis and why it is appropriate

(3) the assumptions that underlie the method

(4) the statistical tests to be conducted

(5) a discussion of results that will answer the research question

Assume you are writing the "methods" section of a research paper to be submitted to a professional journal.

Each of the 2 data sets described employs at least one of the following statistical methods:

(1) Analysis of Variance

(2) Analysis of Covariance

(3) Multivariate Analysis of Variance

(4) Multivariate Analysis of Covariance

(5) Discriminant Analysis

(6) Logistic Regression

(7) Cluster Analysis

(8) Principal Components

(9) Exploratory Factor Analysis

(10) Confirmatory Factor Analysis

.

Problem 2:

Attribution theory is concerned with the cognitive processes that individuals use to explain their own performance in situations where causal relations are ambiguous. Empirical evidence indicates a tendency for individuals to attribute their own successful performance to internal factors, such as effort or ability, while poor performance is attributed to external factors beyond the individual's control. An experiment was conducted to examine the causal reasoning patterns of system users at the conclusion of a competitive, computer-based business game.

Eighty MBA students used what appeared to be different computer models to analyze unexpected variances in manufacturing costs. (Actually, all students utilized the same computer model.) Upon completion, students were paid an amount based on their overall performance: those who were told they performed poorly relative to their peers were paid $5, while those who were told they did well earned $20. (In actuality, the students were randomly assigned to one of the two performance groups.) At the time of payment, participants completed an evaluation form upon which five outcome variables were measured (each on a 7-point Likert scale):

Internal Outcome SAS Variable Names

EFFORT -- amount of effort expended

UND -- how well they understood the cost structure

External Outcome SAS Variable Names

QUALITY -- quality of the computer model used

LUCK -- level of good/bad luck

DIFF -- difficulty of the task itself

The main purpose of the study is to determine whether the means of the outcome variables described above differ depending on performance level (SAS variable defined below).

PERLEVEL = 1 if poor performance ($5),

2 if good performance ($20)

The data are saved in the ATTRIB SAS file. Several observations are listed below.

PERLEVEL EFFORT UND QUALITY LUCK DIFF

1 4 6 5 4 3

1 3 4 6 1 1

1 3 4 4 6 3

1 3 3 5 5 5

1 4 5 5 6 1

2 7 5 4 4 4

2 5 2 1 6 6

2 5 3 3 4 5

2 4 3 1 2 4

2 4 5 4 5 4

Problem 3:

Radio-frequency identification (RFID) is the wireless use of electromagnetic fields to track data. Some industries have already adopted RFID technology (e.g., an RFID tag attached to an automobile during production is used to track its progress through the assembly line), but others have yet to adopt. This study attempts to identify those factors that increase the likelihood or probability of RFID adoption for supply chain management companies.

Data were collected through a Web-based survey of managers who are members of the Institute for Supply Management (ISM). A total of 755 managers participated in the survey. A list of the variables measured for each manager is provided below (SAS variable name given first). The researchers want to use these variables to build an algorithm which accurately predicts whether or not a supply management firm will adopt RFID technology.

ASDOPT -- Firm’s RFID adoption status (1=adopted, 0=not adopted)

NUMIT -- Total number of other information technology adoptions at firm

WLAN – Level of wireless LAN adoption (HI-USE, LO-USE, or NO-USE)

WMS – Level of warehouse management system adoption (HI-USE, LO-USE, or NO-USE)

BAR – Level of barcode adoption (HI-USE, LO-USE, or NO-USE)

P2LS – Level of “pick-to-light” system adoption (HI-USE, LO-USE, or NO-USE)

FIRMTYPE – Domestic (DOM) or International (INT) firm

REVENUE – Firm revenue status (LOW or HIGH)

CHLEADER -- “My firm is obligated to do as the channel/supply chain leader suggests”

(7-point Likert scale where 1=strongly disagree and 7=strongly agree)

QUALITY -- “My firm is concerned with product quality”

(7-point Likert scale where 0=never and 7=always)

SERVICE -- “My firm feels channel/supply chain leader provides services needed”

(7-point Likert scale where 1=strongly disagree and 7=strongly agree)

The data are saved in the RFID SAS file. Several observations are listed below.

CHLEADER QUALITY SERVICE ADOPT WLAN WMS BAR P2LS NUMIT REVENUE FIRMTYPE

4 1 2 1 LO-USE NO-USE LO-USE NO-USE 3 LOW DOM

6 2 4 0 HI-USE NO-USE HI-USE NO-USE 3 LOW DOM

2 1 6 0 NO-USE HI-USE HI-USE NO-USE 4 HIGH DOM

6 4 4 1 HI-USE HI-USE HI-USE NO-USE 5 HIGH INT

4 5 4 0 LO-USE HI-USE HI-USE NO-USE 4 HIGH INT