Time-Varying Effect Sizes for Quadratic Growth Models in Multilevel and Latent Growth Modeling

Abstract

Multilevel and latent growth modeling analysis (GMA) is often used to compare independent groups in linear random slopes of outcomes over time, particularly in randomized controlled trials. The unstandardized coefficient for the effect of group on the slope from a linear GMA can be transformed into a model-estimated effect size for the group difference at the end of a study. Because effect sizes vary nonlinearly in quadratic GMA, the effect size at the end of a study using quadratic GMA cannot be derived from a single coefficient, and cannot be used to estimate effect sizes at intermediate time points with backward extrapolation. This article formulates equations and associated input commands in Mplus for time-varying effect sizes for quadratic GMA. Illustrative analyses that produced these time-varying effect sizes were presented, and a Monte Carlo study found that bias in the effect sizes and their confidence intervals was ignorable.

Keywords:

• confidence intervals
• effect sizes
• latent growth models
• multilevel analysis

Additional information

Funding

This work was supported by National Institutes of Health (NIH)/National Institute on Alcohol Abuse and Alcoholism (NIAAA) grant R01AA025069. The content is solely the responsibility of the author and does not necessarily represent the official views of the NIH or NIAAA.