The effect of number of clusters and cluster size on statistical power and type I error rates when testing random effects variance components in multilevel linear and logistic regression models

Peter C. Austin*, George Leckie

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

9 Citations (Scopus)
405 Downloads (Pure)

Abstract

When using multilevel regression models that incorporate cluster-specific random effects, the Wald and the likelihood ratio (LR) tests are used for testing the null hypothesis that the variance of the random effects distribution is equal to zero. We conducted a series of Monte Carlo simulations to examine the effect of the number of clusters and the number of subjects per cluster on the statistical power to detect a non-null random effects variance and to compare the empirical type I error rates of the Wald and LR tests. Statistical power increased with increasing number of clusters and number of subjects per cluster. Statistical power was greater for the LR test than for the Wald test. These results applied to both the linear and logistic regressions, but were more pronounced for the latter. The use of the LR test is preferable to the use of the Wald test.

Original languageEnglish
Pages (from-to)3151-3163
Number of pages13
JournalJournal of Statistical Computation and Simulation
Volume88
Issue number16
Early online date7 Aug 2018
DOIs
Publication statusPublished - 2 Nov 2018

Keywords

  • hierarchical model
  • multilevel analysis
  • multilevel model
  • Statistical power
  • variance components

Fingerprint Dive into the research topics of 'The effect of number of clusters and cluster size on statistical power and type I error rates when testing random effects variance components in multilevel linear and logistic regression models'. Together they form a unique fingerprint.

Cite this