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Area of Science:

  • Psychological research methods
  • Structural equation modeling
  • Latent variable analysis

Background:

  • Latent variables and interactions are common in psychological research.
  • Latent moderated structural equations (LMS) is a method for analyzing latent interactions in Mplus.
  • Current LMS methods lack traditional fit indices, standardized coefficients, and effect sizes, hindering interpretation.

Purpose of the Study:

  • To provide a tutorial for new users of LMS models.
  • To compile state-of-the-science techniques for assessing LMS model fit.
  • To guide obtaining standardized coefficients and determining latent interaction effect sizes.

Main Methods:

  • Demonstration via a substantive example.
  • Monte Carlo simulation to validate techniques.
  • Compilation of current best practices for LMS model assessment.

Main Results:

  • Developed a sequence for estimating and interpreting LMS models.
  • Provided methods for assessing model fit and effect sizes for latent interactions.
  • Illustrated techniques with a practical example and simulation data.

Conclusions:

  • The techniques enhance the utility of LMS models for psychological research.
  • Addressing limitations facilitates interpretation of latent variable interactions.
  • Extensions for quadratic effects and slope-intercept interactions offer future research avenues.