TY - JOUR
T1 - Power analysis for complex mediational designs using Monte Carlo methods
AU - Thoemmes, Felix
AU - Mackinnon, David
AU - Reiser, Mark
N1 - Funding Information:
This research was supported in part by a U.S. Public Health Service Grant DA09757 to David P. MacKinnon.
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
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U2 - 10.1080/10705511.2010.489379
DO - 10.1080/10705511.2010.489379
M3 - Article
AN - SCOPUS:77954448337
SN - 1070-5511
VL - 17
SP - 510
EP - 534
JO - Structural Equation Modeling
JF - Structural Equation Modeling
IS - 3
ER -