Education | Multivariate Analysis in Educational Research
Y604 | 5986 | Dr. Ginette Delandshere
Students enrolled in the course should have completed Y502 or its
equivalent [Y603 also recommended]. Students should also have prior
knowledge of a computer system for accessing SAS or SPSS software or
have experience with their WINDOWS applications.
This course is based on the premise that the function of statistics is
to formulate arguments for explaining comparative differences and
relationships in data. This course focuses on the General Linear
Model (GLM) and the various forms it takes in the multivariate
context. The forms of the model will be discussed in relationship to
the particular research questions for which they are appropriate. A
range of multivariate statistical analysis procedures are considered
to examine relationships between multiple variables (e.g., multiple
dependent and/or independent variables) and comparisons will be made
to their univariate equivalent. Principal component and factor
analysis will also be covered as a way to reduce the number of
measured variables to a smaller number of scores or underlying
Confirmatory factor analysis and the testing of simple structural
models will also be introduced as well as structural equation modeling
which allows for the examination of multiple relationships among
variables and for taking into account measurement error (using
measurement models for latent variables).
The limitations (i.e., assumptions) and unresolved issues of each
forms of the GLM will be examined. Students will learn to formulate
research questions, to select appropriate analysis procedures, to
conduct statistical analyses, and to report, interpret and write up
narratives of the results in relation to the research questions and
1.To understand the nature and function of multivariate statistics and
to use appropriate procedures to answer specific research questions
2.To carry out statistical analyses and to verify the underlying
3.To interpret computer printouts, report and write up results in
relation to specific research contexts.
Two sets of textbooks have been ordered for this course and you can
work from either set depending to your preference. Set 1 includes the
Sage Publication series that cover the material for this course. Set
2 is a comprehensive textbook. Notes are also made available for each
class session on SiteScape - http://ssf.indiana.edu/gdelands.
Articles and chapters on reserve for this course can be found at
Namboodiri, K. Matrix Algebra, An Introduction #38, Beverly Hills,
CA: Sage Publications.
Lewis-Beck, M.S. (1980). Applied Regression, An Introduction #22,
Beverly Hills, CA: Sage Publications.
Berry, W.D. & Feldman, S. (1985). Multiple Regression in Practice
#50, Beverly Hills, CA: Sage Publications.
Bray, J.H. & Maxwell, S.E. (1985). Multivariate Analysis of Variance
#54, Beverly Hills, CA: Sage Publications.
Klecka, W.R. (1980). Discriminant Analysis #19, Beverly Hills, CA:
Kim, J-O. & Mueller, C. W. (1978). Introduction to Factor Analysis
#13, Beverly Hills, CA: Sage Publications, Inc.
Kim, J-O. & Mueller, C. W. (1978). Factor Analysis: Statistical
Methods and Practical Issues #14, Beverly Hills, CA: Sage
Long, S. Confirmatory Factor Analysis, A Preface to LISREL #33.
Beverly Hills, CA: Sage Publications, Inc.
Long, S. Covariance Structure Models, An Introduction to LISREL #34.
Beverly Hills, CA: Sage Publications, Inc.
Tabacknick, B. G. & Fidell, L. S. (2001). Using Multivariate
Statistics, Fourth Edition.
New York: Harper Collins Publishers, Inc. [T&F]
Additional Reference Books
Abelson, R. P. (1995). Statistics as Principled Argument. Hillsdale,
NJ: Lawrence Erlbaum Associates, Inc.
Jöreskog K. G. (1993). Testing structural equation models. In K. A.
Bollen, J. & Scott Long (Eds.) Testing structural equation models.
Newburyk Park, CA: Sage Publications, Inc.
Cooley, W. W. and Lohnes, P. R. (1985). Multivariate Data Analysis
(2nd. ed.) New York, NY: John Wiley and Sons, Inc.
Grimm, L. G. and Yarnold, P. R. (1995). Reading and Understanding
Multivariate Statistics. DC: American Psychological Association.
Hair, J. F., Anderson, R.E., Tatham, R.L. & Black, W.C. (1992).
Multivariate Data Analysis, (3rd. ed.), Macmillan Publishing Company,
New York, NY.
Harris, R.J. (1985). A Primer of Multivariate Statistics, (2nd. ed.),
Orlando, FL., Academic Press.
Loehlin, J. C. (1992). Latent Variable Models: An Introduction to
Factor, Path and Structural Analysis. (2nd. ed.), Hillsdale, NJ:
Lawrence Erlbaum Associates, Inc.
Morrison, D. F. (1989). Multivariate Statistical Methods, (2nd. ed.),
New York, NY: McGraw-Hill Publishing Company
Pedhazur, E.J. (1982). Multiple Regression in Behavioral Research:
Explanation and Prediction, Second Edition. New York: Holt, Rinehart
Pedhazur, E. J. and Schmelkin, L. (1991). Measurement, Design, and
Analysis: An Integrated Approach, Hillsdale, NJ: Lawrence Erlbaum
Porter, T. M. (1995). Trust in Numbers: The Pursuit of Objectivity
in Science and Public Life. Princeton, NJ: Princeton University
Stevens, J. (1992). Applied Multivariate Statistics for the Social
Sciences, (2nd. ed.), Hillsdale, NJ: Lawrence Erlbaum Associates, Inc.
Tentative Course Outline and Schedule
Week 1 Introduction and Overview
9/3-9/5 Students inventory of interests and experience with research,
statistics and measurement
o Taxonomy of Multivariate Techniques
o Review of univariate analysis techniques
o Assumptions underlying multivariate techniques
Readings: [T&F: Chap. 1 & 2]
9/10 Making Claims with Statistics and Quantification
o Function of Statistics - Meaning of Measurement
[Abelson: Chap.1] - on reserve
[Porter: Chap.4] - on reserve
9/12 Matrix Algebra
9/17-19 Operations, Order, Trace, Determinant
o Sums of Square and Cross-Product (SSCP)
o Inverse, Rank, Eigen Values, Eigen Vectors
Readings: [T&F: Appendix A]
9/24-26 Basic model, assumptions
10/1-3 Analysis of the residuals
10/8-10 Outliers, Multicollinearity
o Types of Multiple Regression
o Part and Partial Correlation
Readings:[T&F: Chap. 4 & 5]
Weeks 7-8 Hotelling's T2 and Manova
10/22-24 Readings:[T&F: Chap. 9]
Week 9-10 Discriminant Analysis
11/5-7 Readings:[T&F: Chap. 11]
Weeks 11-12 Principal Component and Exploratory Factor Analysis
11/19-21 Readings:[T&F: Chap. 13]
Week 13-15 Structural Equation Modeling
11/26-28 Confirmatory Factor Analysis
12/3-5 Covariance Structure Models
12/10-12 Readings:[T&F: Chap. 14]
Week 16 _EXAMINATION #2_
Course Assignments and Evaluation
I expect all assigned readings to be done as specified in class for
each session. Readings are assigned to complement in-class
presentations and discussions, and to formalize understanding of the
As an enrolled student, you will complete two written assignments, and
two examinations. The assignments are designed to evaluate your
conceptual understanding of the statistical analysis procedures, the
computations involved, how the analyses relate to the research
questions as well as the nature of the interpretations and inferences
made based on the analysis of data. Short homework (e.g., matrix
algebra exercises, reading research articles) will also be assigned
for some of the topics and will have to be turned in on a timely
Each assignment will be evaluated according to a set of criteria that
will be communicated as part of the assignment. The assignments,
homework, and examinations will contribute to the final grade as
Regression assignment: 20%
EFA assignment 20%
Examination #1 25%
Examination #2 25%
Grading procedures are in accordance with the Bulletin for the
Graduate Program of the School of Education. "Incomplete" will not be
granted except for extremely unusual circumstances. Plagiarism will
result in an automatic "F" for the course.
Other Guidelines for the Course
Students are encouraged to discuss the course material among
themselves and to assist each other with data analysis. The written
part of the assignments should, however, reflect individual student's
Labs are designed to provide additional assistance to students and
include: (1) assistance in setting up computer programs, (2)
assistance with homework and assignments, and (3) review of concepts
and procedures. Students should come to lab prepared to ask