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Maximum likelihood estimation of a social relations structural equation model.

Steffen Nestler1, Oliver Lüdtke2,3, Alexander Robitzsch2,3

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Summary
This summary is machine-generated.

This study introduces a new method combining the social relations model (SRM) with structural equation modeling (SEM) for analyzing interpersonal perceptions. This approach offers a more reliable way to investigate complex relationships within SRM data.

Keywords:
maximum likelihood estimationsocial relations modelstructural equation modeling

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Area of Science:

  • Psychology
  • Social Psychology
  • Quantitative Psychology

Background:

  • The social relations model (SRM) is a key framework for understanding interpersonal perceptions, behaviors, and judgments.
  • Current SRM analyses often rely on a two-step approach, potentially yielding unreliable estimates of true SRM effects.
  • Investigating multivariate relations between SRM effects is currently challenging.

Purpose of the Study:

  • To introduce a novel integrated framework combining the social relations model (SRM) with structural equation modeling (SEM).
  • To provide a method for estimating parameters within this combined framework using maximum likelihood (ML) estimation.
  • To demonstrate the utility and statistical properties of the proposed integrated model.

Main Methods:

  • Integration of the social relations model (SRM) within the structural equation modeling (SEM) framework.
  • Application of maximum likelihood (ML) estimation for model parameters.
  • Illustrative example from personality psychology and a simulation study to assess statistical properties.

Main Results:

  • The proposed integrated SRM-SEM model allows for direct investigation of multivariate relations between SRM effects.
  • Maximum likelihood (ML) estimation provides a robust method for parameter estimation.
  • Simulation studies indicate the model performs well across various conditions, enhancing reliability.

Conclusions:

  • The combined SRM-SEM framework offers a statistically sound and more reliable approach for analyzing complex interpersonal dynamics.
  • This integration overcomes limitations of previous two-step methods in SRM research.
  • An R package (srm) is available, facilitating the application of these advanced methods in psychological research.