**Psychology 204B: Causal Modeling
of Correlational Data**

*Dr. Dean Keith Simonton, Distinguished
Professor of Psychology*

**192 Young Hall 2:10-4:00**

**Contents**

**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 *R*^{2}, 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.

**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.

**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.

**Grading**

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.

**Exam 1**: Get on 1-29 and return on 1-30 (by 4 pm)

**Exam 2: **Get on 2-19 and return on 2-20 (by 4 pm)

**Exam 3: **Get on 3-12 and return on 3-13 (by 4 pm)

*E-mail*:

Dean Simonton (instructor) - dksimonton@ucdavis.edu

Joel Steele (teaching assistant) - jssteele@ucdavis.edu

Last Revised: August 10, 2011