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

Author

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  • Steffen Nestler

    (University of Leipzig)

  • Katharina Geukes

    (University of Münster)

  • Roos Hutteman

    (University of Utrecht)

  • Mitja D. Back

    (University of Münster)

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.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:psycho:v:82:y:2017:i:4:d:10.1007_s11336-016-9546-5
    DOI: 10.1007/s11336-016-9546-5
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    References listed on IDEAS

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    1. Charles Bond & Brian Lashley, 1996. "Round-robin analysis of social interaction: Exact and estimated standard errors," Psychometrika, Springer;The Psychometric Society, vol. 61(2), pages 303-311, June.
    2. Dorff, Cassy & Ward, Michael D., 2013. "Networks, Dyads, and the Social Relations Model," Political Science Research and Methods, Cambridge University Press, vol. 1(2), pages 159-178, December.
    3. Rosseel, Yves, 2012. "lavaan: An R Package for Structural Equation Modeling," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i02).
    4. Peter D. Hoff, 2005. "Bilinear Mixed-Effects Models for Dyadic Data," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 286-295, March.
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    Cited by:

    1. Brian Gin & Nicholas Sim & Anders Skrondal & Sophia Rabe-Hesketh, 2020. "A Dyadic IRT Model," Psychometrika, Springer;The Psychometric Society, vol. 85(3), pages 815-836, September.
    2. Steffen Nestler & Oliver Lüdtke & Alexander Robitzsch, 2020. "Maximum likelihood estimation of a social relations structural equation model," Psychometrika, Springer;The Psychometric Society, vol. 85(4), pages 870-889, December.
    3. 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.
    4. Jeremy Koster & Brandy Aven, 2018. "The effects of individual status and group performance on network ties among teammates in the National Basketball Association," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-18, April.

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