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

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Summary

Researchers extended the Social Relations Model (SRM) for longitudinal data analysis. The new social relations growth model tracks interpersonal judgments over time, offering insights into group dynamics and individual relationships.

Keywords:
linear mixed modellongitudinal datarestricted maximum likelihoodsocial relations model

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Area of Science:

  • Social psychology
  • Quantitative psychology
  • Group dynamics

Background:

  • The Social Relations Model (SRM) is a standard framework for analyzing interpersonal judgments and behaviors within groups.
  • The original SRM is limited to cross-sectional data, restricting its application in studying changes over time.

Purpose of the Study:

  • To extend the Social Relations Model (SRM) to accommodate longitudinal data.
  • To introduce the social relations growth model for analyzing repeated interpersonal judgments over time.
  • To provide methods for estimating model parameters and analyzing covariate effects on growth variability.

Main Methods:

  • Development of the social relations growth model, representing repeated SRM judgments as a function of time.
  • Application of restricted maximum likelihood (REML) for parameter estimation.
  • Computation of covariate effects on interindividual and interdyad variability in growth.

Main Results:

  • The study demonstrates the feasibility of estimating parameters for the social relations growth model.
  • Methods for computing covariate effects on growth variability are presented.
  • A simulation study confirmed the suitability of the proposed model for longitudinal SRM data.

Conclusions:

  • The social relations growth model effectively extends the SRM to longitudinal data.
  • The proposed methods allow for a nuanced understanding of how interpersonal judgments evolve within groups over time.
  • This extension provides a valuable tool for researchers studying dynamic social processes.