A Comparison of Methods for Specifying Optimal Random Effects Structures

Wen Luo, Haoran Li

Research output: Contribution to journalArticlepeer-review

Abstract

Using Monte Carlo simulations, this study compared the performance of various approaches to the specification of random effects structures in linear mixed effects models (LMMs), including the minimal approach, the maximal approach, the forward search, the backward search, and the all-possible structures approach. The results showed that if the predictor of interest is at the within-cluster level or involves a cross-level interaction, the maximal approach, the best-path forward search, and the best-path backward search are all desirable methods. If the predictor of interest is at the cluster level, it is not essential to specify random slopes of Level-1 predictors. In addition, it is important to specify random slopes of within-cluster control variables, as they can increase the statistical power for testing the main within-cluster variables, especially when the sample size is small and the variance of the random slope of the control variable is large.

Original languageEnglish (US)
Pages (from-to)365-386
Number of pages22
JournalMethodology
Volume19
Issue number4
DOIs
StatePublished - Dec 2023

Bibliographical note

Publisher Copyright:
© 2023 Hogrefe Publishing GmbH. All rights reserved.

Keywords

  • linear mixed effects models (LMMs)
  • mathematics subject
  • model selection
  • model specification
  • random effects structures

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