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Tackling Longitudinal Round-Robin Data: A Social Relations Growth Model

Published online by Cambridge University Press:  01 January 2025

Steffen Nestler ,
Katharina Geukes ,
Roos Hutteman  and
Mitja D. Back
Steffen Nestler*
Affiliation:
University of Leipzig
Katharina Geukes
Affiliation:
University of Münster
Roos Hutteman
Affiliation:
University of Utrecht
Mitja D. Back
Affiliation:
University of Münster
*
Correspondence should be made to Steffen Nestler,University of Leipzig, Neumarkt. 9-19, 04109 Leipzig,Germany. Email: steffen.nestler@uni-leipzig.de
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Abstract

The social relations model (SRM) is commonly used in the analysis of interpersonal judgments and behaviors that arise in groups. The SRM was developed only for use with cross-sectional data. Here, we introduce an extension of the SRM to longitudinal data. The social relations growth model represents a person’s repeated SRM judgments of another person as a function of time. We show how the model’s parameters can be estimated using restricted maximum likelihood, and how the effects of covariates on interindividual and interdyad variability in growth can be computed. An example is presented to illustrate the suggested approach. We also present the results of a small simulation study showing the suitability of the social relations growth model for the analysis of longitudinal SRM data.

Keywords

social relations modellinear mixed modellongitudinal datarestricted maximum likelihood
Type
Original Paper
Information
Psychometrika , Volume 82 , Issue 4 , December 2017 , pp. 1162 - 1181
Copyright
Copyright © 2016 The Psychometric Society

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Footnotes

This article is dedicated to Irmgard Laufer.

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