EDEP 604: Multiple Regression in Behavioral Research (3 credits)
Advanced applications of general linear
model to complex problems of data analysis. Relation of analysis of
variance and covariance to regression analysis. Nonlinearity and
treatment of missing data. Pre: EDEP 601 and
602, or consent. (Cross-listed as PSY 612 and SW 654)
Modified: April 6, 2006
Pedhazur, E. J. (1997).
Multiple regression in behavioral research (3rd ed.).
Orlando, FL: Harcourt Brace.
Ward, J. H., Jr., & Jennings,
E. (1973). Introduction to linear models.
[Originally published by Prentice-Hall, now available from
The Institute for Job and Occupational Analysis (IJOA),
10010 San Pedro, Suite 440, San Antonio, Texas 78216
Aiken L & West S. (1991). Testing Interactions in Multiple Regression.
Hillsdale NJ: Lawrence Erlbaum.
Cohen J, Cohen P, Aiken L, & West S. (2003). Applied Multiple
Regression/Correlation Analysis for the Behavioral Sciences. 3rd ed.
Hillsdale NJ: Lawrence Erlbaum.
Darlington, R. B. (1990). Regression and linear models. New York: McGraw-Hill.
Judd, C. M., & McClelland, G.
H. (1989). Statistical analysis: A model comparison
approach. Orlando, FL: Harcourt Brace Janovich. Their
Pedhazur, E. J., & Schmelkin, L.
P. (1991). Measurement, design, and analysis: An
integrated approach. Hillsdale, NJ: Elrbaum.
Wickens, T. D. (1995). The
geometry of multivariate statistics. Hillsdale, NJ: Erlbaum.
Online Resources: (barely scratch the
surface of what's available on the Internet)
The student is strongly encouraged to make use of these Internet
resouces/links as a way to reinforce one's understanding of basic
Relevant Text Online
Interactions in Multiple Linear Regression, Latent Curve Analysis, and
Hierarchical Linear Modeling: Interactive calculation tools for
establishing simple intercepts, simple slopes, and regions of significance
(Kristopher J. Preacher, Patrick J. Curran, and Daniel J. Bauer
University of North Carolina at Chapel Hill)
Primer on Interaction Effects in Multiple Linear Regression
(Kristopher J. Preacher, University of North Carolina at Chapel Hill)
of the Importance of Centering Continuous Variables Before Creating
Interaction Terms (Scot W. McNary) (.pdf)
- James Algina's
pages (syllabi, SAS and SPSS code, papers, etc.)
Statistics: Manifest Variables Analysis (Stephen Lea, University
Electronic Statistical Textbook (from StatSoft)
HyperStat (David M. Lane, Rice University)
- The Little
Handbook of Statistics Practice (Gerard E. Dallal, Tufts
Regression (G. David Garson, North Carolina State University)
Statistics: Concepts, Models, and Applications (David W.
Stockburger, Missouri State University)
Probability Theory -- The Logic of Science (E. T. Jaynes,
Washington University – St. Louis)
- Behrens, J. T. & Yu, C. H. (1994, June). The
visualization of multi-way interactions and higher-order terms in
multiple regression. Paper presented at the annual meeting of the
- Chow, S. L. (1996). Precis
of Statistical significance: Rationale, Validity and
Utility. London, Sage.
- Jones, L. V. & Tukey, J. W. (2000).
A sensible reformulation of the significance test.
Psychological Methods, 5, 411-414.
- Laviolette, M. (1994). Linear regression: The computer as a teaching tool. Journal of Statistics Education, 2(2).
- Osborne, J. W., & Waters, E. (2002). Four assumptions of
multiple regression that researchers should always test.
Practical Assessment, Research & Evaluation,
- Smith, B. & Sechrest, L. (1991). Treatment of aptitude by treatment interactions.
Journal of Consulting and Clinical Psychology,
- Walker, M. E. (1999). Commentary on Greenwald et al. (1996). Effect sizes and p values: What should be reported and what should be replicated? In Psychophysiology, 33, 175-183. (Michael E. Walker, Ohio State University)
- Ward, J. H., Jr. & Fountain, R. L. (1996). More problem solving power: Exploiting prediction models and statistical software in a one-semester course. Journal of Statistics Education, 4(3).
Calculators and Online Applications
- The Comprehensive R
Archive Network (CRAN)
- gretl: Gnu Regression,
Econometrics and Time-series Library (Allin Cottrell, Wake Forest
Regression: Free Statistical Software for Microsoft Excel (Dave
Steppan, Joachim Werner, & Bob Yeater)
MacAnova (not just for Macs) (Gary W. Oehlert and Christopher
Bingham, University of Minnesota)
- Mx (Michael Neale, Virginia Commonwealth University)
(William G. Miller, Iowa State University)
PQRS (Probability Calculator for the PC)
- |Stat (Gary Perlman,
UC San Diego)
(Kalimuthu Krishnamoorthy, University of Louisiana-Lafayette)
- StatLib (Carnegie Mellon
- ViSta - The Visual
Statistics System (Forrest W. Young, University of North
Data Set Libraries
A. Review of Ordinary Least Squares (OLS) and the General Linear Model (GLM)
1. The Measurement Model
2. The Structural Model
B. Review of Coding Schemes for Categorical Variables
1. Dummy coding
2. Contrast coding
C. Null Hypothesis Testing
1. Comparing Nested Models
2. The F-ratio
D. Analysis of Variance, Analysis of Covariance, Multiple Regression, and the GLM
E. GLM and "Non-linear" Relationships
1. Linear and Non-linear Transformations
2. Polynomial Regression
F. Missing Data on Explanatory Variables
G. Categorical Outcome Variables
1. Discriminant Function Analysis (OLS)
2. Maximum Likelihood Estimation
a. Logistic regression
b. Log-linear models
If you have difficulty with any of the concepts presented in the
text or lectures, please consult one or more of the links above or the
syllabus for one or more of the courses (with associated links) on my
home page. If you are unable to
satisfactorily resolve any questions by consulting the appropriate
links, please make an appointment with me at your earliest
Questions or comments to:
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