Model Misspecification and Assumption Violations With the Linear Mixed Model
A Meta-Analysis
Bibliographic Data
| ID | 3270907 |
|---|---|
| Authors | Brandon Lebeau (0000-0002-1265-8761, University of Iowa, corresponding author), Yoon Ah Song (University of Iowa), Wei Cheng Liu (University of Iowa) |
| Year | 2018 |
| Volume | 8 |
| Issue | 4 |
| Pages | 215824401882038 |
| Publication date | 2018-10-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | SAGE Open (JOURNAL) |
| Journal identifiers | ISSN: 2158-2440 • E-ISSN: 2158-2440 |
| Publisher | SAGE Publications Inc (PUBLISHER) |
| DOI | 10.1177/2158244018820380 |
| OpenAlex | W2801386804 |
| Language | EN |
| Citations received | 4 |
| References cited | 30 |
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
Econometrics · Empirical research · Fixed effects model · Generalized linear mixed model · Linear model · Linear regression · Meta-analysis · Meta-regression · Mixed model · Moderation · Monte Carlo method · Multilevel model · Panel data · Random effects model · Regression analysis · Statistics · Type I and type II errors · Computer Science · Forecasting Techniques and Applications · Mathematics · Medicine · Statistical Methods and Bayesian Inference · Statistical Methods and Inference
Analysis of Longitudinal Data
Applied Longitudinal Data Analysis
Applied Longitudinal Analysis
Ggplot2
The Handbook of Research Synthesis and Meta-Analysis
Random-Effects Models for Longitudinal Data
Transformations Related to the Angular and the Square Root
Monte Carlo Experiments
The influence of violations of assumptions on multilevel parameter estimates and their standard errors
Sufficient Sample Sizes for Multilevel Modeling
Conducting Meta-Analyses in R with the metafor Package
Hierarchical linear models
Multilevel Statistical Models
The Case for Use of Simple Difference Scores to Test the Significance of Differences in Mean Rates of Change in Controlled Repeated Measurements Designs
The Impact of Misspecifying the Within-Subject Covariance Structure in Multiwave Longitudinal Multilevel Models
Effects of Misspecifying the First-Level Error Structure in Two-Level Models of Change
Design and Analysis of Monte Carlo Experiments
| Citations per year | 0.8 |
|---|---|
| Citation span | 2021 - 2026 (6) |
| Highly cited | No |