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Modelling nonlinear moderation effects with local structural equation modelling (LSEM): A non-technical introduction.

Tuo Liu1, Ruyi Ding2, Zhonghuang Su2

  • 1Institute of Psychology, Goethe-Universität Frankfurt am Main, Frankfurt, Germany.

International Journal of Psychology : Journal International De Psychologie
|October 19, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces local structural equation modelling (LSEM), a nonparametric method for analyzing nonlinear moderation effects in psychological research. LSEM overcomes limitations of traditional approaches, enabling flexible exploration and testing of complex relationships.

Keywords:
Local structural equation modellingModerationNonlinearity

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Area of Science:

  • Psychological and Behavioural Sciences
  • Quantitative Psychology
  • Statistical Modelling

Background:

  • Investigating moderation effects, where a third variable influences the strength of a relationship, is crucial in psychological research.
  • Traditional structural equation modelling (SEM) approaches often focus on linear moderation, potentially missing nonlinear effects.
  • Existing methods for continuous moderators can be limited, requiring moderator categorization or pre-specification of functional forms.

Purpose of the Study:

  • To introduce local structural equation modelling (LSEM) as a nonparametric approach for analyzing moderation effects.
  • To demonstrate the application of LSEM for detecting and testing nonlinear moderation without prior assumptions.
  • To showcase the flexibility of LSEM using the R-sirt package with an empirical dataset.

Main Methods:

  • Introduction to local structural equation modelling (LSEM) in a non-technical manner.
  • Implementation of LSEM using the R-sirt package for analyzing nonlinear moderation.
  • Demonstration of both exploratory and confirmatory analyses of moderation functions.

Main Results:

  • LSEM effectively analyzes nonlinear moderation effects without the limitations of traditional SEM approaches.
  • The R-sirt package facilitates versatile implementation of LSEM for various research scenarios.
  • The nonparametric nature of LSEM allows for the discovery of unexpected nonlinear moderation patterns.

Conclusions:

  • LSEM offers a powerful and flexible nonparametric alternative for studying nonlinear moderation in psychological research.
  • This approach enhances the ability to uncover complex relationships influenced by continuous moderators.
  • Researchers can utilize LSEM for both hypothesis-driven and data-driven investigations of moderation.