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Estimating statistical power for structural equation modeling (SEM) is difficult, especially for non-nested models. This study presents a Monte Carlo simulation method for accurate a priori power estimation in SEM model selection for developmental science research.

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Area of Science:

  • Developmental Psychology
  • Quantitative Psychology

Background:

  • Structural Equation Modeling (SEM) is crucial for understanding psychological constructs.
  • Estimating statistical power and sample size for SEM, particularly for non-nested models, is challenging.
  • Existing rules of thumb for SEM sample size are often inadequate.

Purpose of the Study:

  • To introduce a Monte Carlo simulation approach for estimating a priori statistical power in SEM.
  • To provide a practical guide for applying this method to non-nested SEM comparisons.
  • To address the need for robust power analysis in developmental science research.

Main Methods:

  • Utilized Monte Carlo simulations to estimate statistical power.
  • Developed a step-by-step procedure for power analysis in SEM model selection.
  • Applied the method to a developmental memory research example.

Main Results:

  • Demonstrated the feasibility of Monte Carlo simulations for SEM power estimation.
  • Provided a reliable method for determining sample size for non-nested SEM comparisons.
  • Highlighted the limitations of generic heuristics for SEM power analysis.

Conclusions:

  • Monte Carlo simulations offer a powerful tool for a priori statistical power estimation in SEM.
  • This approach enhances the accuracy of sample size determination for complex model comparisons.
  • The proposed method is valuable for researchers in developmental science and other fields utilizing SEM.