Predicting social relations model effects from conditional expectations.

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  • Author(s): Bond CF;Bond CF; Malloy TE; Malloy TE
  • Source:
    Psychological methods [Psychol Methods] 2023 Feb; Vol. 28 (1), pp. 137-151. Date of Electronic Publication: 2022 Jan 27.
  • Publication Type:
    Journal Article
  • Language:
    English
  • Additional Information
    • Source:
      Publisher: American Psychological Association Country of Publication: United States NLM ID: 9606928 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1939-1463 (Electronic) Linking ISSN: 1082989X NLM ISO Abbreviation: Psychol Methods Subsets: MEDLINE
    • Publication Information:
      Original Publication: Washington, DC : American Psychological Association, c1996-
    • Subject Terms:
      Interpersonal Relations* ; Motivation*; Humans ; Models, Theoretical
    • Abstract:
      The Social Relations Model (SRM) is a conceptual and mathematical model of interpersonal responses in dyads. The SRM permits estimation of responses of one to many (the actor effect) and the responses of many to the one (the partner effect) at the individual level of analysis. The SRM also permits estimation of the unique responses of actors and partners in specific dyadic arrangements (the relationship effect). During the four decades that the SRM has been used empirically, most attention was focused on estimation of variance and covariance of components. More recently, second stage modeling has occurred in which SRM effect estimates are used as variables in multivariate models. Consequently, it has become important to have good predictions of SRM actor, partner, and relationship effects. A method proposed by Warner, Kenny, and Stoto has been used to predict these effects. Here we propose an alternative matrix-based estimation method that predicts the latent SRM random effects from their conditional expected values given observed data. Analytic work and Monte Carlo simulations indicate that our conditional-expectation predictions of SRM effects are more valid and precise than the traditional predictions. They will improve second-stage Social Relations Modeling and also have practical uses as well (in, e.g., determining employee salary raises). (PsycInfo Database Record (c) 2023 APA, all rights reserved).
    • Grant Information:
      US-Israel Binational Science Foundation
    • Publication Date:
      Date Created: 20220127 Date Completed: 20230410 Latest Revision: 20230411
    • Publication Date:
      20240628
    • Accession Number:
      10.1037/met0000469
    • Accession Number:
      35084888