Richard Gonzalez
Center Director, Research Center for Group Dynamics, Institute for Social Research
Co-Director, BioSocial Methods Collaborative
Amos N Tversky Collegiate Professor, Psychology and Statistics, LSA
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 |
About Me
I received my PhD in 1990 from Stanford University in Psychology. I worked with Phoebe Ellsworth studying psychology and law and with Amos Tversky studying mathematical modeling and judgment and decision making. I spent seven years at the University of Washington’s Psychology department, a sabbatical year at Princeton University, and have been at the University of Michigan’s Psychology department since 1997. More about me >>
Research
My research interests focus on judgment and decision making (JDM). Given that so many topics in psychology are related to JDM it makes it look as though I work on many different topics. Actually, I see a simple theme across all my research. I am interested in how people make judgments and what influences their decisions and choices. More about Research >>
People
I’ve been fortunate to work with amazing colleagues, coauthors, collaborators, and students throughout my career. Read about People >>
Teaching
- Theories of Social Psychology
- General Linear Modeling Course
- Multivariate Statistics
- Generalized Linear Modeling Course
- Structural Equations Modeling Course
- Statistics Animation Page
- Design Science PhD Program
Recent Posts
Testing perceptions of running shoes during treadmill and outdoor running.
Richard Gonzalez Center Director, Research Center for Group Dynamics, Institute for Social ResearchDirector, BioSocial Methods Collaborative, RCGDAmos N Tversky Collegiate Professor, Psychology and Statistics, LSAProfessor of Marketing, Stephen M Ross School of...
Comparison of common amplitude metrics in event-related potential analysis
We applied statistical theory to compare several common amplitude metrics for event-related potential analysis of EEG data
A new approach to assessing the subjective experience of a running shoe
Developed a multidimensional approach to assessing the subjective experience of a running shoe’s ride
Machine learning and the selection of statistical interactions
Machine learning does a great job of selecting variables to include in a predictive model. But it will not always obey some desired properties that we implement in most analytic strategies, such as including main effect versions of a predictor if that predictor is included in the model in the form of an interaction with other variables. We compare several existing algorithms and propose a new one to address this issue.