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Likelihood Estimation of the Multivariate Social Relations Model

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  • Steffen Nestler

    (University of Leipzig)

Abstract

The social relations model (SRM) is a mathematical model that can be used to analyze interpersonal judgment and behavior data. Typically, the SRM is applied to one (i.e., univariate SRM) or two variables (i.e., bivariate SRM), and parameter estimates are obtained by employing an analysis of variance method. Here, we present an extension of the SRM to an arbitrary number of variables and show how the parameters of this multivariate model can be estimated using a maximum likelihood or a restricted maximum likelihood approach. Overall, the two likelihood approaches provide consistent and efficient parameter estimates and can be used to investigate a multitude of interesting research questions.

Suggested Citation

  • Steffen Nestler, 2018. "Likelihood Estimation of the Multivariate Social Relations Model," Journal of Educational and Behavioral Statistics, , vol. 43(4), pages 387-406, August.
    • Handle: RePEc:sae:jedbes:v:43:y:2018:i:4:p:387-406
    DOI: 10.3102/1076998617741106
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    References listed on IDEAS

    as
    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. 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.
    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.
    Full references (including those not matched with items on IDEAS)

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