Abstract

Applied researchers often include mediation effects in applications of advanced methods such as latent variable models and linear growth curve models. Guidance on how to estimate statistical power to detect mediation for these models has not yet been addressed in the literature. We describe a general framework for power analyses for complex mediational models. The approach is based on the well-known technique of generating a large number of samples in a Monte Carlo study, and estimating power as the percentage of cases in which an estimate of interest is significantly different from zero. Examples of power calculation for commonly used mediational models are provided. Power analyses for the single mediator, multiple mediators, 3-path mediation, mediation with latent variables, moderated mediation, and mediation in longitudinal designs are described. Annotated sample syntax for Mplus is appended and tabled values of required sample sizes are shown for some models.

Original languageEnglish (US)
Pages (from-to)510-534
Number of pages25
JournalStructural Equation Modeling
Volume17
Issue number3
DOIs
StatePublished - 2010

Fingerprint

Power Analysis
Mediation
Monte Carlo method
Design Method
mediation
Monte Carlo methods
Mediator
Growth Curve Model
Latent Variable Models
Statistical Power
Latent Variables
Monte Carlo Study
Model
syntax
Estimate
Guidance
Percentage
Sample Size
Path
Zero

ASJC Scopus subject areas

  • Modeling and Simulation
  • Decision Sciences(all)
  • Economics, Econometrics and Finance(all)
  • Sociology and Political Science

Cite this

Power analysis for complex mediational designs using Monte Carlo methods. / Thoemmes, Felix; Mackinnon, David; Reiser, Mark.

In: Structural Equation Modeling, Vol. 17, No. 3, 2010, p. 510-534.

Research output: Contribution to journalArticle

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