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

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  • Brandon LeBeau
  • Yoon Ah Song
  • Wei Cheng Liu

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.

Suggested Citation

  • Brandon LeBeau & Yoon Ah Song & Wei Cheng Liu, 2018. "Model Misspecification and Assumption Violations With the Linear Mixed Model: A Meta-Analysis," SAGE Open, , vol. 8(4), pages 21582440188, December.
    • Handle: RePEc:sae:sagope:v:8:y:2018:i:4:p:2158244018820380
    DOI: 10.1177/2158244018820380
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    References listed on IDEAS

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