Model Misspecification and Assumption Violations With the Linear Mixed Model: A Meta-Analysis

Brandon LeBeau, Yoon Ah Song and Wei Cheng Liu

SAGE Open, 2018, vol. 8, issue 4, 2158244018820380

Abstract: This meta-analysis attempts to synthesize the Monte Carlo (MC) 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 MC simulations in identifying simulation conditions. The current meta-analysis will use the empirical type I error rate as the effect size and MC 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 MC studies will be explored.

Keywords: linear mixed model; longitudinal data; type I error rate; meta-analysis (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:sae:sagope:v:8:y:2018:i:4:p:2158244018820380

DOI: 10.1177/2158244018820380

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