Model misspecification and assumption violations with the linear mixed model: A meta-analysis

Mixed Models
Longitudinal
This meta-analysis attempts to synthesize the Monte Carlo literature for the linear mixed model under a longitudinal framework.
Authors

Brandon LeBeau

Yoon Ah Song

Wei Cheng Liu

Published

December 1, 2018

Abstract

This meta-analysis attempts to synthesize the Monte Carlo literature for the linear mixed model under a longitudinal framework. The meta-analysis aims to inform researchers about conditions that are important to consider when evaluating model assumptions and adequacy. In addition, the meta-analysis may be helpful to those wishing to design future Monte Carlo simulations in identifying simulation conditions. The current meta-analysis will use the empirical type I error rate as the effect size and Monte Carlo simulation conditions will be coded to serve as moderator variables. The type I error rate for the fixed and random effects will be explored as the primary dependent variable. Effect sizes were coded from 13 studies, resulting in a total of 4,002 and 621 effect sizes for fixed and random effects respectively. Meta-regression and proportional odds models were used to explore variation in the empirical type I error rate effect sizes. Implications for applied researchers and researchers planning new Monte Carlo studies will be explored.

Citation

LeBeau, Brandon, Song, Yoon Ah, Liu, Wei Cheng (2018). Model misspecification and assumption violations with the linear mixed model: A meta-analysis. **Sage Open, 8 (4).