Psychology 204B: Causal Modeling of Correlational Data

Dr. Dean Keith Simonton, Distinguished Professor of Psychology

192 Young Hall 2:10-4:00
 

Contents

General Information

Topical Outline

Course Requirements
 
 

General Information

Course Goals

The course is divided into three parts:

A. Multiple Regression/Correlation Analysis - Bivariate correlation and regression methods are generalized to accommodate multiple independent variables. Partial and semipartial correlations, partial regression coefficients, and multiple correlation are all introduced as responses to the "third variable" problem in causal inference. Also discussed are the issues of inflated R², multicollinearity and suppression effects, and standardized versus unstandardized parameters. This section concludes with an introduction to executing multiple regression/correlation analyses using commercially available computer software.

• Click here for more detailed outline of this section.
• To download handouts, go to here.

B. Advanced Techniques and Topics - Other issues in multiple regression will be discussed, as time permits. These include (a) nominally scaled dependent and independent variables, (b) interaction effects and curvilinear functions, (c) the consequences of outliers, autoregressive residuals, and violations of multivariate normality, and (d) missing data. Incorporated throughout this section is a discussion of how the results are normally presented in the professional literature.

• Click here for more detailed outline of this section.
• To download handouts, go to here.

C. Path Analysis and Structural Equation Models - An introduction to the basic principles of formulating and testing causal models using systems of linear equations. Discussion of how to use covariance algebra, the basic theorem, or the tracing rule to work out the implications of alternative linear models. Although the focus will be on equation-by-equation ordinary least squares parameter estimation, an overview will be given of such system estimation procedures as generalized least squares and maximum likelihood. In addition, as time permits, we will discuss (a) model misspecifications, especially measurement errors, correlated disturbances, and reciprocal causality or feedback loops, (b) the identification problem, criteria for identification, and the testing of underidentified models, (c) nominally scaled endogenous and exogenous variables, (d) multiple indicators in latent variable models, and (e) the use of such specialized software as EQS and LISREL.

• Click here for more detailed outline of this section.
• To download handouts, go to here.

The three exams are worth 25% each, the research project 15%, and the assignments 10%. All exams and assignments must be turned on time for full credit. Otherwise, one-third of a grade will be deducted each day one is late.  More information about the research project available here.

Please note that I assume that all exams and homework represent individual work.  Therefore, collaboration on the take-home exams or homework assignments will be considered violations of the student code of conduct.
 
 

Jacob Cohen, Patricia Cohen, Stephen G. West, & Leona S. Aiken.
Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences (3rd ed.).
Mahwah, NJ: Lawrence Erlbaum, 2003.

This is a much-expanded version of a classic text.  First published in 1975, it went through a second edition in 1983.  Given that track record, it will provide a standard text for years to come. However, the authors were sometimes careless about proofing the equations and numbers. Hence, I have posted errata for the 2nd and 3rd printing (provided by Gary Stockwell, a former TA). An additional erratum here (provided by Dr. A.W. Hoogendoorn, Department of Social Research Methodology, VU University Amsterdam). Finally, Robert Grissom (SFSU) pointed out that on page 84 (3rd printing), which gives Equation 3.5.8 for the semipartial r as a function of beta, the multiple correlation in the radical should be squared.