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Richard GonzalezRichard 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

Articles in Teaching

Analyzing multivariate dyadic data for exchangeable dyads

This was our first paper on dyadic analysis. In retrospect, one of our main contributions was more pedagogical in that we showed how to get solid intuition about dyadic data and presented a framework in which to derive estimates and their standard errors. The framework illuminated several aspects of dyadic data, including why a correlation of dyadic means is sometimes difficult to interpret. Our results are identical to multilevel models using maximum likelihood estimation.

Teaching concept generation to engineers

We show how our heuristic techniques help engineering designers explore the concept space to achieve more novel solutions and explore the problem/solution space more effectively.

Data analysis for distinguishable dyads

In this paper we present methods for the analysis of dyadic data when the two members are distinguishable (e.g., gender distinguishes the members in a heterosexual couple). We develop the pairwise model for the distinguishable case and show that it provides identical parameter estimates as a latent level model in a structural equations model framework.

Measuring the degree of ordinal association between two variables

In this paper Tom Nelson and I review several alternative measures of association. Most researchers make ordinal statements such as “when one variable goes up, the other goes down.” But then they assess such an ordinal statement with a Pearson correlation or a linear regression. There are better measures available as reviewed in this paper. We also address the thorny issue of how to handle ties in data.

A sales pitch for modern Bayesian data analysis

A basic chapter introducing psychologists to the world of modern Bayesian statistics. We cut out a lot of the dogma and go into sales pitch mode on the benefits of going Bayesian. If we pique your interest in learning more about what Bayesian tools can offer, then we consider the chapter a success.

Learning how to use R

I’ll be teaching a course on R through Statistical Horizons in Philadelphia Oct 2-3, 2015. For more information…

A piece on interval scaling from the point of view of classic psychometrics

Not many researchers use the classic psychometric scaling work such as successive intervals any more. The work goes back to the early days of psychometrics (e.g., Fechner, Thurstone) and even some early mathematical psychology (e.g., Coombs). There still is use for these classic models in understanding the meaning of rating scales.

The correlation of a difference score with another variable is difficult to interpret

It is difficult to interpret a correlation when one variable is a difference score. We show in this paper how the same correlation can arise from many different patterns, with each pattern implying a different interpretation. We give examples that arise in research on dyads (e.g., studying the relation between husband and wife salary on marial satisfaction). We provide recommendations for how to test research questions involving differences and discrepancies.