Richard 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 |
Introduction to the analysis of dyadic data
A chapter in the APA Handbook of Research Methods in Psychology. The chapters provides a basic introduction to the analysis of dyadic data.
Gonzalez, R. & Griffin, D. (2012). Dyadic data analysis. Handbook of Research Methods
in Psychology: Vol 3. Data Analysis and Research Publication, H. Cooper, Editor-in-Chief, New York: American Psychological Association, 439-450.
doi:10.1037/13621-022 PDF
Abstract
The study of interdependence contributes to a rich understanding of social life. Does a husband’s depression influence his wife’s depression? Does his depression influence her marital satisfaction? What is the similarity of the husband and wife’s depression within a couple and does that similarity predict other variables such as marital satisfaction? What predicts the degree of similarity in depression between husband and wife? Research questions such as these involve data that span two individuals, so we say the data are interdependent. Such research questions frequently include multiple variables and sometimes involve longitudinal data. What makes these research questions psychologically interesting is that they focus on interpersonal processes. Of course, if one wants to study interpersonal processes, then it would be useful to collect data and use analytic procedures that permit the assessment and testing of interpersonal processes.
The analysis of interdependent data presents special issues because the covariance across individuals needs to be addressed in the analyses. Failure to account for these interpersonal correlations can introduce bias into an analysis but, more important, consideration of these interpersonal correlations allows one to assess interesting interpersonal psychological processes. The violation of independence is the ugly pebble that can be transformed into the pearl of interdependence. In this chapter, we illustrate a few analytic techniques that go beyond “fixing” data for independence violations to providing rich models that permit the researcher to assess psychological processes of interdependence. Interdependence is not treated as a nuisance that needs to be corrected but rather as one of the key psychological parameters to model. We view this chapter as introductory, focusing on the special case of dyads, and review a few of the analysis techniques that are currently available.
