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Correlated predictors can make distinguishing interaction and quadratic effects difficult in structural equation models. Simultaneous estimation prevents misleading nonlinear effects, ensuring accurate moderation analysis.

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Area of Science:

  • Social sciences
  • Quantitative psychology
  • Statistical modeling

Background:

  • Interaction and moderation effects are crucial in social science research.
  • Correlated predictors can obscure the distinction between interaction and quadratic effects.
  • Incorrect model specification can lead to misleading findings in nonlinear structural equation models.

Purpose of the Study:

  • To investigate the consequences of specification errors in nonlinear structural equation models.
  • To clarify the distinction between interaction and quadratic effects when predictors are correlated.
  • To provide accurate methods for analyzing nonlinear effects in latent variable models.

Main Methods:

  • A Monte Carlo simulation study was employed.
  • The study examined different types of specification errors in nonlinear structural equation models.
  • The analysis focused on the interplay between interaction and quadratic effects.

Main Results:

  • Fitting interaction-only models when quadratic effects exist leads to erroneous moderation detection.
  • Conversely, fitting quadratic-only models when interaction effects exist also yields incorrect results.
  • Simultaneously estimating both interaction and quadratic effects provides accurate results.

Conclusions:

  • Simultaneous estimation of interaction and quadratic effects is essential to avoid spurious nonlinear effects.
  • Applied researchers are advised to consider simultaneous estimation for robust moderation analysis.
  • This approach enhances the reliability of findings in social science research involving nonlinear relationships.