MA684 Final Project
Spring 2019
This is an individual project—please do your own work. Some discussion with other students around
computer work for the project is permitted, but you should formulate and perform the analyses on
your own, and write up your results on your own. (A good rule of thumb: only write answers when
you are completely alone.) Questions about the content of the project or programming issues can be
directed to the instructor or TF.
This project makes up a substantial proportion of your grade, so please provide an organized,
professional, and well-edited write-up. Please write up your results in paragraph form—do not simply
annotate computer output. This is a statistics class, so please present appropriate statistical detail—
identify the statistical methods that you use, explain how you reach your conclusions, report test
statistics along with P-values to make it clear what information is being reported. Please report your
results in the context of the problem. If your write-up is complete, we should not need to refer to your
computer output, but please submit your computer output as an appendix with the project. (The
syntax will be submitted on BlackBoard, while the report will be turned in on paper at the final exam.)
You will be graded on (1) running the analyses correctly (worth 50%), (2) providing correct syntax
(all analyses must be run using syntax, but for small calculations such as p-values or effect sizes; worth
10%), and (3) your professional write-up of the solutions (worth 40%). The final write-up should be
written as though it were a final report being provided to a research client…ideally, your write-up will
be complete enough so we won’t have to refer to the computer output. The document should follow
the 5 C’s of communication: clear, concise, correct, cogent & comprehensive.
The project is due Thursday May 9 (3:00pm). Projects should be submitted at start of final exam
(printout report) and submit syntax on BlackBoard. If you encounter any problems, please email a
copy of your final project to both the instructor ([email protected]) and the TF
and please include your name on the project.
You are running analyses for a moderately-sized company located in the Pacific Northwest. (Note, this
is a hypothetical scenario with hypothetical data.) The company states that it values diversity and
equal opportunities for all of its employees, regardless of gender, race or any other categorization.
They have hired you to help them examine their employee evaluation and promotion process.
The employees in this data set were with the company for at least two years (one full year beyond the
probationary period), and this subset of the data includes only employees classified at the supervisory
level or below (no managers or executives) at the time of the most recent evaluation cycle. The data
on each employee is the following:
emp_ID: a unique code for each employee in the data set
jobrating: a score assigned by the employee’s direct supervisor on a 0–100 scale.
salary: 12-month adjusted FT salary, in USD
gender_F: dummy variable for gender identification, coded 0 for male and 1 for female
race: categorical variable, coded 0 for white, 1 for Asian, and 2 for other
promote: dummy variable indicating if employee was promoted within the past 11-months
There are no missing data in this final version of the data set.
In addition to these concrete variables, the employees also completed the “Personality Questionnaire”.
Items on this self-rated instrument asked how well the following word/phrase describes the employee
on a 1 (not at all like me) to 5 (very much like me) scale:
do a thorough job reliable perseveres
original imaginative shy
reserved quiet sticks to a plan
curious inventive
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The ultimate goal of the study is to determine what sort of people get good job ratings and get
promoted. In particular, the clients are interested in the association between personality type and job
ratings. However, they also want to examine any other possible relationships that may or may not
require attention.
For this analysis, there are a few necessary conversions/transformations for the data. First, be sure
that you have coded the race variable as a categorical (factor) variable, or have created the necessary
dummy variables. Please use “white” as the reference group. For gender, no changes are necessary,
but please use “male” as the reference group. As is common with variables such as salary, it is better
to work with the log(salary) instead of the untransformed value. Create a new variable, and use this
log_salary variable in your analyses. Lastly, convert the questionnaire data into a smaller set of
subscales, as indicated next.
First, it is necessary to summarize the data from the personality questionnaire. Conduct a principal
components analysis with promax (non-orthogonal) rotation to determine the number of factors.
Then conduct an exploratory factor analysis on the 11 personality variables to develop summary
measures of personality. (In your final write-up, be sure to indicate ?How many summary measures
are needed to describe personalities? ?How well do these summary measures capture the information
from the 11 personality variables? ?What is measured by these summary measures? and for all of your
answers, how did you reach this conclusion?)
Examine all possible bivariate relationships among the data: examine multicollinearity (between pairs
of independent variables) and possible confounding (between indep. vars. and the dependent
variable). Of particular importance are questions regarding whether gender and/or race are related to
the dependent variables (with and without controlling for the possible confounding variables). You
probably will want to explore models that examine the effect of gender after controlling for other
variables.
Based on your decisions to generate scales for the personality measures, run a regression analysis to
predict job rating from all of the available information. Next, run a logistic regression analysis to
predict promotions from all of the available information (including job rating). Construct the best
models (and justify the choice for those models). Assess model fit.
For all dichotomous variable analyses, se sure to report and interpret relevant odds-ratios.
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