Analyzing Longitudinal Social Relations Model Data Using the Social Relations Structural Equation Model

Steffen Nestler, Oliver Lüdtke and Alexander Robitzsch
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Steffen Nestler: University of Münster, Germany
Alexander Robitzsch: Leibniz Institute for Science and Mathematics Education, Kiel, Germany

Journal of Educational and Behavioral Statistics, 2022, vol. 47, issue 2, 231-260

Abstract: The social relations model (SRM) is very often used in psychology to examine the components, determinants, and consequences of interpersonal judgments and behaviors that arise in social groups. The standard SRM was developed to analyze cross-sectional data. Based on a recently suggested integration of the SRM with structural equation models (SEM) framework, we show here how longitudinal SRM data can be analyzed using the SR-SEM. Two examples are presented to illustrate the model, and we also present the results of a small simulation study comparing the SR-SEM approach to a two-step approach. Altogether, the SR-SEM has a number of advantages compared to earlier suggestions for analyzing longitudinal SRM data, making it extremely useful for applied research.

Keywords: social relations model; latent growth model; autoregressive model; longitudinal data; structural equation model (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:47:y:2022:i:2:p:231-260

DOI: 10.3102/10769986211056541

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