Accuracy of Estimates and Statistical Power for Testing Meditation in Latent Growth Curve Modeling

Abstract

The latent growth curve modeling (LGCM) approach has been increasingly utilized to investigate longitudinal mediation. However, little is known about the accuracy of the estimates and statistical power when mediation is evaluated in the LGCM framework. A simulation study was conducted to address these issues under various conditions including sample size, effect size of mediated effect, number of measurement occasions, and R2 of measured variables. In general, the results showed that relatively large samples were needed to accurately estimate the mediated effects and to have adequate statistical power, when testing mediation in the LGCM framework. Guidelines for designing studies to examine longitudinal mediation and ways to improve the accuracy of the estimates and statistical power were discussed.

Keywords: accuracy of mediated effects; indirect effects; latent growth curve modeling; longitudinal mediation; statistical power.

Figures

Figure 1

A parallel process latent growth model for mediation. X = independent variable; Y = outcome; M = mediator; η1 = intercept (initial status) factor of mediator; η3 = intercept (initial status) factor of outcome; η2 = slope (growth rate) factor of mediator; η4 = slope (growth rate) factor of outcome.

Figure 2

Empirical power: Three measurement occasions.

Figure 3

Empirical power: Five measurement occasions.