BSAD 6314 Section 001
Multivariate Statistics
Spring 2015
Professor
Wendy J. Casper, Ph. D.
Office: Business Building 233
E-mail: wjcasper@uta.edu
Phone: (817) 272-1133
Office Hours: By appointment
Course Time & Location:
BSAD 6314 Section 001; Tuesdays, 2:00 pm – 4:50 pm; COBA 139
Course Website:
Materials posted on Blackboard
Course Description:
This course is designed to help you to effectively apply, interpret, and evaluate different
multivariate statistical techniques. Throughout the course there will be an emphasis on both
conceptual understanding and the development of practical "how-to" skills. Topics covered
in the sequence are organized in terms of complexity, beginning with a broad overview,
moving into regression, and ending with structural equation modeling.
Business scholars have made use of a broad range of methods and analytical strategies to
address questions of interest. Because each approach to answering research questions
involves trade-offs, researchers have often found it necessary to employ a combination of
analytical techniques to reach any firm conclusions. A major goal of this course is to
facilitate decision making within these constraints.
We will discuss a variety of advanced statistical techniques. Throughout the semester, you
will gain hands-on experience through projects and learn how to draw statistical and
substantive conclusions from results of analyses. You will be asked to prepare written
summaries of results using either Academy of Management style or style guidelines for
other journals in your field. If you use style guidelines other than those of the Academy of
Management or the American Psychological Association, please provide a copy of these
guidelines to me along with your first project so I can refer to them as I evaluate your
projects.
Learning Objectives:
By the end of this course students will be able to:
1. Choose the appropriate multivariate technique to answer specific research questions
2. Describe the pros and cons of using various techniques to control for missing data
3. Describe the assumptions required to use various multivariate techniques including
regression, canonical correlation, logistic regression, discriminant analysis,
MAVOVA, and factor analysis.
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4. Use SPSS to run statistical analysis in regression, logistic regression, discriminant
analysis, and factor analysis.
5. Interpret the results of data analysis by examining SPSS output from regression,
canonical correlation, logistic regression, discriminant function analysis,
MANOVA, and factor analysis.
Required Text:
Hair, J. F., Black, W.C., Babin, B. J., Anderson, R.E., Tatham, R. L. 2006.
Multivariate Data Analysis (6th Edition) Upper Saddle River, NJ: Prentice Hall.
Recommended Supplemental Materials:
(1)
Style guidelines for your discipline
Examples:
Academy of Management Journal Style Guide.
http://aom.pace.edu/amjnew/style_guide.html
American Psychological Association Style Guide.
http://www.apastyle.org/
(2) SPSS and SPSS Statistical Procedures Companion book. In this class we will use the
statistics software program SPSS and I will ask you to hand in your output and syntax
from SPSS with your projects. You should have version 22 or some other recent
version. It is strongly recommended that you purchase SPSS and install it on your
own computer, or it is possible to rent SPSS as well
(http://www.onthehub.com/spss/). You will need to get the "Statistics Base
Gradpack." Please do not buy the “Student version” of SPSS because it limits the
number of variables and cases and does not allow the use of syntax so it will not meet
our needs for this class. If you are a behavioral student, you should definitely buy
SPSS as this is the most widely used statistical software in the behavioral area and
you will continue to use it in your research and dissertation. If STATA, SAS, or
another statistics package is more widely used in your area, you may want to rent
SPSS just for this semester.
Course Requirements:
Course requirements include: (1) three project assignments and brief write-ups of results in
appropriate format; (2) a midterm exam; (3) a final exam; and (4) a final project.
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Final Paper: The final paper should examine research questions using a set of data. You
must use at least two multivariate techniques that we cover in class. Results must be
written in the style guidelines in your discipline. Students are encouraged to submit their
papers to a national conference in their discipline. In order to ensure you are on track
with the paper early in the semester, you will be required to hand in an outline of what
you plan to do one month into the semester. The outline should address the following:
1. What data set will you use? How did you obtain access to this data? (Please
ensure approvals to use data have been obtained prior to handing in your outline)
What is your sample size? What variables are included in this data set?
2. What are the hypotheses or research questions you plan to answer? (Have at least
3 hypotheses or questions).
3. What multivariate statisical techniques will you use to test each
hypothesis/answer each question? (You will need to ensure you meet assumptions of
each technique).
If you do not already have datasets to use from your own research projects or
collaborations with another professor, I you should talk with your faculty members about
possible datasets that you might use for your project. You can also look at websites ehere
data are publically provided and use a data set in the public domain. For example, see
https://www.icpsr.umich.edu/icpsrweb/landing.jsp for data available to UTA faculty and
students. If you use a data set provided to you by another professor or colleague, it is a
good idea to discuss expectations regarding ongoing research up front with whoever
provides the data to you. For example, if any presentations or publications emerge from
your project in my class, whoever provided you the data is typically a coauthor.
Class Policies
Due Dates. Projects and the final paper must be handed in by the beginning of class on
the day they are due. Anything that is late will receive 50% of the possible points if
handed in within a day. No points will be given for any work that is handed in more than
one day late.
Collaborative Work. You are encouraged to work together to conduct data analysis for
projects and prepare for exams. However, each student must independently write up data
analysis results for projects. Students should complete exams and the final paper without
assistance from other students.
Make ups. Please do not miss exams. Make-up exams will not be allowed except under
conditions of documented severve illness or emergency.
Attendance: At the University of Texas at Arlington, taking attendance is not required.
Rather, each faculty member is free to develop his or her own methods of evaluating
students’ academic performance, which includes establishing course-specific policies on
attendance. As the instructor of this class, I will not take attendance. However, as PhD
students, you are expected to be highly committed to you learning, and thus, I expect
attendance problems will not be a problem. If a specific problem or another commitment
arises that prevents you from attending class, please discuss this with me. You will be
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responsible for all material covered in class and for knowledge of any announcements or
changes to the schedule made in class, even if you are unable to attend class.
Course Grades:
30%
20%
20%
30%
Evaluation of the assigned projects (10% each)
Midterm exam
Final exam
Final project
Final grades: A (90%); B (80%); C (70%); D (60%); F (below 60%)
Drop Policy: Students may drop or swap (adding and dropping a class concurrently)
classes through self-service in MyMav from the beginning of the registration period
through the late registration period. After the late registration period, students must see
their academic advisor to drop a class or withdraw. Undeclared students must see an
advisor in the University Advising Center. Drops can continue through a point two-thirds
of the way through the term or session. It is the student's responsibility to officially
withdraw if they do not plan to attend after registering. Students will not be automatically
dropped for non-attendance. Repayment of certain types of financial aid administered
through the University may be required as the result of dropping classes or withdrawing.
For more information, contact the Office of Financial Aid and Scholarships
(http://wweb.uta.edu/aao/fao/).
Americans with Disabilities Act: The University of Texas at Arlington is on record as
being committed to both the spirit and letter of all federal equal opportunity legislation,
including the Americans with Disabilities Act (ADA). All instructors at UT Arlington are
required by law to provide "reasonable accommodations" to students with disabilities, so
as not to discriminate on the basis of that disability. Any student requiring an
accommodation for this course must provide the instructor with official documentation in
the form of a letter certified by the staff in the Office for Students with Disabilities,
University Hall 102. Only those students who have officially documented a need for an
accommodation will have their request honored. Information regarding diagnostic criteria
and policies for obtaining disability-based academic accommodations can be found at
www.uta.edu/disability or by calling the Office for Students with Disabilities at (817)
272-3364.
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Law “Title IX” such that no member of the UT Arlington community shall, on the basis
of sex, be excluded from participation in, be denied the benefits of, or be subjected to
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Academic Integrity: Students enrolled all UT Arlington courses are expected to adhere
to the UT Arlington Honor Code:
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I pledge, on my honor, to uphold UT Arlington’s tradition of academic integrity, a
tradition that values hard work and honest effort in the pursuit of academic
excellence.
I promise that I will submit only work that I personally create or contribute to
group collaborations, and I will appropriately reference any work from other
sources. I will follow the highest standards of integrity and uphold the spirit of the
Honor Code.
UT Arlington faculty members may employ the Honor Code as they see fit in their
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submitted. Per UT System Regents’ Rule 50101, §2.2, suspected violations of
university’s standards for academic integrity (including the Honor Code) will be referred
to the Office of Student Conduct. Violators will be disciplined in accordance with
University policy, which may result in the student’s suspension or expulsion from the
University.
Electronic Communications and Meetings Outside Class: I am available to meet with
students outside of class to discuss questions and concerns. If you wish to meet with me
please send me an email and we will arrange a mutually convenient time to meet. UT
Arlington has adopted MavMail as its official means to communicate with students about
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graduation. Information about activating and using MavMail is available at
http://www.uta.edu/oit/cs/email/mavmail.php. Since the UTA spam filter will sometimes
filter out email from other sources (i.e., yahoo, gmail), please use your MavMail account
to communicate with me via email. You are responsible for all updated information
about the class (schedule changes, etc.) that is communicated to you through your UTA
email. Therefore, not being aware of a change because you did not read UTA email will
not be deemed an acceptable reason for lack of awareness about changes pertinent to the
course.
Student Feedback Survey: At the end of each term, students enrolled in classes
categorized as “lecture,” “seminar,” or “laboratory” shall be directed to complete an
online Student Feedback Survey (SFS). Instructions on how to access the SFS for this
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to solicit, gather, tabulate, and publish student feedback is required by state law; students
are strongly urged to participate. For more information, visit http://www.uta.edu/sfs.
Final Review Week: A period of five class days prior to the first day of final
examinations in the long sessions shall be designated as Final Review Week. The purpose
of this week is to allow students sufficient time to prepare for final examinations. During
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this week, there shall be no scheduled activities such as required field trips or
performances; and no instructor shall assign any themes, research problems or exercises
of similar scope that have a completion date during or following this week unless
specified in the class syllabus. During Final Review Week, an instructor shall not give
any examinations constituting 10% or more of the final grade, except makeup tests and
laboratory examinations. In addition, no instructor shall give any portion of the final
examination during Final Review Week. During this week, classes are held as scheduled.
In addition, instructors are not required to limit content to topics that have been
previously covered; they may introduce new concepts as appropriate.
Emergency Exit Procedures: Should we experience an emergency event that requires us
to vacate the building, students should exit the room and move toward the nearest exit, on
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never take an elevator but should use the stairwells. Faculty members and instructional
staff will assist students in selecting the safest route for evacuation and will make
arrangements to assist individuals with disabilities.
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Writing Center: The Writing Center, 411 Central Library, offers individual 40 minute
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workshops on grammar and specific writing projects. Visit https://uta.mywconline.com/
to register and make appointments. For hours, information about the writing workshops
we offer, scheduling a classroom visit, and descriptions of the services we offer
undergraduates, graduate students, and faculty members, please visit our website at
www.uta.edu/owl/.
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CLASS SCHEDULE
This course is a dynamic process, subject to change. You are responsible for maintaining
awareness of changes in class scheduling if you have missed class.
Date
Topic
Jan 20
Overview of Multivariate Statistics
Hair, Chapter 1
Bobko, P. 1990. Multivariate Correlational Analysis. In Dunnette, M. D. & Hough,
L. M. (Eds.) Handbook of Industrial/Organizational Psychology (2nd Edition), Volume 1,
pp. 637-686. Palo Alto, CA: Consulting Psychologists Press.
Jan 27
Data Cleaning and Multivariate Techniques
Hair, Chapter 2
Orr, J. M., Sackett, P. R., & Dubois, C. L. Z. 1991. Outlier detection and treatment
in I/O psychology: A survey of researcher beliefs and an empirical illustration. Personnel
Psychology, 44: 473-486.
Roth, P.L., & Switzer, F.S. 1995. A monte carlo analysis of missing data
techniques in a HRM setting. Journal of Management, 21: 1003-1023.
Myers, T. A. (2011). Goodbye, Listwise Deletion: Presenting Hot Deck
Imputation as an Easy and Effective Tool for Handling Missing Data. Communication
Methods and Measures, 5:4, 297-310, DOI: 10.1080/19312458.2011.624490
Griggs et al. Example of Use of mean substitution
Feb 3
Multiple Regression
Hair, Chapter 4
St. John, C. H. & Roth, P. L. 1999. The impact of cross-validation adjustments on
estimates of effect size in business policy and strategy research. Organizational Research
Methods, 2: 157-174.
Feb 10
Regression: Mediation & Moderation
Aiken, L. S., & West, S. G. 1991. Multiple regression: Testing and interpreting
interactions (Ch. 2-4, pp. 9-61; Ch. 7, pp. 116-138). Newbury Park, CA: Sage.
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Baron, R. M., & Kenny, D. A. 1986. The moderator-mediator variable distinction
in social psychological research: Conceptual, strategic, and statistical considerations.
Journal of Personality and Social Psychology, 51: 1173-1182.
Edwards, J. R., & Lambert, L. S. (2007). Methods for integrating moderation and
mediation: A general analytical framework using moderated path analysis. Psychological
Methods, 12, 1-22.
Zhao, X. Lynch, J. G. & Chen, Q. (2010). Reconsidering Baron and Kenny:
Myths and Truths about Mediation Analysis. Journal of Consumer Research, 37, 197206.
Useful aid for graphing interactions:
http://www.jeremydawson.co.uk/slopes.htm
Feb 17
Canonical Correlation
Hair, Chapter 8 from 5th edition (see Blackboard list of readings)
Feb 24
Logistic Regression: Dichotomous Dependent Variables
DUE Outline of Final Paper. Include data set choosen, hypotheses to test and
techniques to use to test them
Hair, pp. 269-275; 366-382
Green, G. H., Boze, B. V., Choundhury, A. H., & Power, S. 1998. Using logistic
regression in classification. Marketing Research, 5-31.
Peng, C. J., Lee, K. L., Ingersoll, G. M. (2002). An introduction to logistic
regression analysis and reporting. The Jorunal of Educational Research, 96, 3-14.
Mar 3
Discriminant Analysis
Hair, pp. 276-355
Betz, N. E. 1987. Use of discriminant analysis in counseling psychology research.
Journal of Counseling Psychology, 34: 393-403.
March 10
SPRING BREAK
March 17
Review for Exam I
March 24
Exam I
Hair, Chapter 6
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Haase, R. F., & Ellis, M. V. 1987. Multivariate analysis of variance. Journal of
Counseling Psychology, 34: 393-403.
March 31
MANOVA
April 7 and 14
Factor Analysis
Hair, Chapter 3
Conway J. M., & Huffcutt A.I. 2003. A review and evaluation of exploratory
factor analysis practices in organizational research. Organizational Research Methods, 6:
147-168.
Ford, J. K., MacCallum, R. C., & Tait, M. 1986. The application of exploratory
factor analysis in applied psychology: A critical review and analysis. Personnel
Psychology, 39: 291-314.
April 21
Structural Equation Modeling
Hair, Chapter 10 and 11
Anderson, J. C., & Gerbing, D. W. 1988. Structural equation modeling in
practice: A review and recommended two-step approach. Psychological Bulletin, 103,
411-423.
Mulaik, S. A., James, L. R., Van Alstine, J., Bennett, N., Lind, S., & Stilwell, C.
D. 1989. Evaluation of goodness-of-fit indices for structural equation models.
Psychological Bulletin, 105: 430-445.
April 28 and May 5
Special Topics in Data Analysis, Review Exam II
Atinc, G., Simmering, M. J., Kroll, M. J. (2012). Control variable use and
reporting in macro- and micro-management research. Organizational Research Methods,
15, 57-74.
Carlson, K. D. & Wu, D. (2011). The illusion of statistical control: Control
variable practice in management research. Organizational Research Methods, 15, 413435.
Zyphur, M. J. (2009). When mindsets collide: Switching analytical mindsets to
advance organizational science. Academy of Management Review, 34, 677-688.
Bedeian, A. G., Taylor, S. G., & Miller, A. N. (2010). Management science on the
credibility bubble: Cardinal sins and various misdemeanors. Academy of Management
Learning & Education, 9(4), 715-725.
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May 9
Exam II; DUE Final Paper
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