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

• Published in: Structural equation modeling Vol. 18; no. 2; pp. 195 - 211
• Main Author: Cheong, JeeWon
• Format: Journal Article
• Language: English
• Published: United States Taylor & Francis Group 01.01.2011; Psychology Press
• Subjects: Accuracy; accuracy of mediated effects; Computation; Effect Size; Effects; Estimates; indirect effects; latent growth curve modeling; longitudinal mediation; Measurement; Mediation; Power; Predictor Variables; Sample Size; Simulation; Statistical Analysis; statistical power; Structural Equation Models; accuracy of mediated effects; statistical power; indirect effects; latent growth curve modeling; longitudinal mediation
• ISSN: 1070-5511; 1532-8007
• DOI: 10.1080/10705511.2011.557334

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 R 2 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.