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

Author

Listed:
  • Steffen Nestler

    (University of Münster, Germany)

  • Oliver Lüdtke
  • Alexander Robitzsch

    (Leibniz Institute for Science and Mathematics Education, Kiel, Germany)

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.

Suggested Citation

  • Steffen Nestler & Oliver Lüdtke & Alexander Robitzsch, 2022. "Analyzing Longitudinal Social Relations Model Data Using the Social Relations Structural Equation Model," Journal of Educational and Behavioral Statistics, , vol. 47(2), pages 231-260, April.
    • Handle: RePEc:sae:jedbes:v:47:y:2022:i:2:p:231-260
    DOI: 10.3102/10769986211056541
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    References listed on IDEAS

    as
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    3. Steffen Nestler & Katharina Geukes & Roos Hutteman & Mitja D. Back, 2017. "Tackling Longitudinal Round-Robin Data: A Social Relations Growth Model," Psychometrika, Springer;The Psychometric Society, vol. 82(4), pages 1162-1181, December.
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    5. Rosseel, Yves, 2012. "lavaan: An R Package for Structural Equation Modeling," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i02).
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