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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

Basic Texts:

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

Other References:

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 Book; Their Course.

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.

Data Sets and SAS Control Files

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 statistical concepts.

Relevant Text Online

Online Articles



Calculators and Online Applications

Statistical Packages

Downloadable Software

Data Set Libraries


Discussion Lists


Course Outline:

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 convenience.

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