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Maximum likelihood estimation of a social relations structural equation model

Author

Listed:
  • Steffen Nestler

    (University of Münster)

  • Oliver Lüdtke

    (Leibniz Institute for Science and Mathematics Education
    Centre for International Student Assessment)

  • Alexander Robitzsch

    (Leibniz Institute for Science and Mathematics Education
    Centre for International Student Assessment)

Abstract

The social relations model (SRM) is widely used in psychology to investigate the components that underlie interpersonal perceptions, behaviors, and judgments. SRM researchers are often interested in investigating the multivariate relations between SRM effects. However, at present, it is not possible to investigate such relations without relying on a two-step approach that depends on potentially unreliable estimates of the true SRM effects. Here, we introduce a way to combine the SRM with the structural equation modeling (SEM) framework and show how the parameters of our combination can be estimated with a maximum likelihood (ML) approach. We illustrate the model with an example from personality psychology. We also investigate the statistical properties of the model in a small simulation study showing that our approach performs well in most simulation conditions. An R package (called srm) is available implementing the proposed methods.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:psycho:v:85:y:2020:i:4:d:10.1007_s11336-020-09728-z
    DOI: 10.1007/s11336-020-09728-z
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    References listed on IDEAS

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    1. Steffen Nestler, 2016. "Restricted Maximum Likelihood Estimation for Parameters of the Social Relations Model," Psychometrika, Springer;The Psychometric Society, vol. 81(4), pages 1098-1117, December.
    2. 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.
    3. Li, Heng, 2006. "The covariance structure and likelihood function for multivariate dyadic data," Journal of Multivariate Analysis, Elsevier, vol. 97(6), pages 1263-1271, July.
    4. 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.
    5. Rosseel, Yves, 2012. "lavaan: An R Package for Structural Equation Modeling," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i02).
    6. 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:

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    2. Stefanos Balaskas & Aliki Panagiotarou & Maria Rigou, 2023. "Impact of Personality Traits on Small Charitable Donations: The Role of Altruism and Attitude towards an Advertisement," Societies, MDPI, vol. 13(6), pages 1-17, June.

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