Richard GonzalezRichard Gonzalez

Center Director, Research Center for Group Dynamics, Institute for Social Research
Director, BioSocial Methods Collaborative, RCGD
Amos N Tversky Collegiate Professor, Psychology and Statistics, LSA
Professor of Marketing, Stephen M Ross School of Business
Professor of Integrative Systems and Design, College of Engineering

 

E-mail: Email Richard Gonzalez
Address: Research Center for Group Dynamics
Institute for Social Research
University of Michigan
426 Thompson Street
Ann Arbor, Michigan 48106
Phone: 734-647-6785

Teaching

Teaching Related Sites:

Theories of Social Psychology

I periodically teach the doctoral-level introduction to the history, theory, and models of social psychology. The course examines both classic and contemporary empirical approaches to research problems in the field.

Click here for a copy of the syllabus from the last time I taught that course (F2008).

General linear modeling course

This is the first semester of a two semester graduate statistics sequence. The sequence covers ANOVA and regression in the fall term and multivariate techniques, including basic structural equation modeling, in the winter term. You should plan on taking both courses. Taking only Psychology 613 wouldn’t make much sense, and I rarely let students in Psychology 614 who have not also taken Psychology 613.

This course emphasizes practical techniques for analyzing data and developing intuition for understanding the techniques used in Psychology. I do not cover the mathematics of the relevant statistical theory. If you are interested in the mathematical detail, you should consider taking courses in the statistics department. Keep in mind that a solid background in calculus, linear algebra, real analysis, etc., may be needed to excel in a more mathematically-oriented statistics course. This possibility should be discussed with the individual student.

Here is a syllabus from the last time I taught this course.

The lecture notes for this course are posted here. I typically finish the fall term with lecture notes #8.

Background for Psychology 613

Entering students ask how they can prepare for the first year statistics sequence. A good strategy is to review your notes from an undergraduate statistics course, or work through an undergraduate statistics book over the summer. You should be familiar with measures of central tendency (means, medians), measures of variability (variance, interquartile range), graphical devices (boxplot, scatterplot), the logic of hypothesis testing, the notion of a confidence interval, and details surrounding one and two-sample t-tests. The year-long statistics sequence is self-contained, but I do assume that you know these basic topics so come prepared to the first lecture.

Some of you may benefit from taking a background course the summer before your first year in graduate school. One possibility is to take a summer course through ICPSR. Probably the most relevant preparation courses are Introduction to Statistics and Data Analysis I.

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Multivariate statistics

This course is a continuation of Psychology 613 from the fall term. It is not a standalone course because it builds on material from Psychology 613. I pick up in January at the point where we left off before the break in December. I typically do not allow students into this course unless they had Psychology 613 with me. If you are looking for a one semester multivariate statistics course I suggest taking the 400-level course offered through the statistics department.

Here is a syllabus from the last time I taught this course.

The lecture notes for this course are posted here. I typically begin the term with lecture notes #8 (at the point I left off in December) and finish the term with lecture notes #13.

Generalized linear modeling course

This is a graduate-level course on generalized linear models and generalized linear mixed models. I have taught this course three times through the Ross Business School (BA860). I will likely teach this course again in Winter 2012. Applications in discrete choice theory are discussed, and different estimation techniques such as maximum likelihood and Bayesian approaches are reviewed. Models related to GLM and GLMM, such as proportional odds models and ordinal regression, are also reviewed.

Structural Equations Modeling course

This is a graduate-level course on structural equations modeling. I have taught this course four times through the Ross Business School (BA870). I’m not scheduled to teach this course, though a different form may materialize in 2012. I also cover basic multilevel models, latent growth models, and latent growth mixture models, and review different estimation techniques such as maximum likelihood and Bayesian approaches.

Statistical Animations Page

Over the years I have developed several animations and demos to facilitate the teaching of statistical concepts. I’ve organized many of them into a website, which you can access here. This project was partially funded through the Center for Research on Learning and Teaching.

I’m working on more so check this page again soon.

Design Science PhD Program

The University of Michigan offers a PhD in Design Science. Click here for information on the program.