Document Type

Article

Peer Reviewed

1

Publication Date

12-22-2018

Journal/Book/Conference Title

SAGE Open

DOI of Published Version

10.1177/2158244018820380

Abstract

This meta analysis attempts to synthesize the Monte Carlo literature for the linear mixed model under a longitudinal framework. The empirical type I error rate will serve 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. 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.

Keywords

Linear Mixed Model, Longitudinal Data, Type I Error Rate, Meta Analysis

Journal Article Version

Version of Record

Published Article/Book Citation

LeBeau, B., Song, Y. A., & Liu, W. C. (2018). Model Misspecification and Assumption Violations With the Linear Mixed Model: A Meta-Analysis. SAGE Open. https://doi.org/10.1177/2158244018820380

Rights

Copyright © 2018 LeBeau et al.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

Share

COinS
 

URL

https://ir.uiowa.edu/pq_pubs/1