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A tutorial on assessing statistical power and determining sample size for structural equation models.

Lisa J Jobst1, Martina Bader1, Morten Moshagen1

  • 1Department of Psychological Research Methods.

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Summary
This summary is machine-generated.

This tutorial illustrates statistical power analysis for structural equation modeling (SEM) to improve sample size planning. Conducting power analyses for SEM ensures reliable and replicable research findings, especially for small effects.

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Area of Science:

  • Social Sciences
  • Psychology
  • Statistical Modeling

Background:

  • Structural Equation Modeling (SEM) is widely used in social sciences.
  • Many SEM studies lack statistical power analysis for sample size planning.
  • This oversight can impact the reliability of research findings.

Purpose of the Study:

  • To provide a tutorial on conducting statistical power analyses for SEM.
  • To demonstrate methods for sample size planning in SEM research.
  • To encourage the use of power analysis for more replicable results.

Main Methods:

  • Step-by-step illustration of a priori, post hoc, and compromise power analyses.
  • Utilizing the R package semPower for practical demonstrations.
  • Applying methods to various SEM applications, including model fit and parameter estimation.

Main Results:

  • Demonstration of power analyses for overall model fit, global model comparisons, and individual parameters.
  • Application to multigroup SEM contexts, such as measurement invariance.
  • Guidance on planning sample sizes for expected small to medium effects.

Conclusions:

  • Statistical power analysis is crucial for robust SEM research.
  • Implementing power analysis enhances the reliability and replicability of study outcomes.
  • Thoughtful sample size planning is essential for valid scientific conclusions in SEM.