Model selection of GLMMs in the analysis of count data in single-case studies: A Monte Carlo simulation

Research output: Contribution to journalArticlepeer-review

Abstract

Generalized linear mixed models (GLMMs) have great potential to deal with count data in single-case experimental designs (SCEDs). However, applied researchers have faced challenges in making various statistical decisions when using such advanced statistical techniques in their own research. This study focused on a critical issue by investigating the selection of an appropriate distribution to handle different types of count data in SCEDs due to overdispersion and/or zero-inflation. To achieve this, I proposed two model selection frameworks, one based on calculating information criteria (AIC and BIC) and another based on utilizing a multistage-model selection procedure. Four data scenarios were simulated including Poisson, negative binominal (NB), zero-inflated Poisson (ZIP), and zero-inflated negative binomial (ZINB). The same set of models (i.e., Poisson, NB, ZIP, and ZINB) were fitted for each scenario. In the simulation, I evaluated 10 model selection strategies within the two frameworks by assessing the model selection bias and its consequences on the accuracy of the treatment effect estimates and inferential statistics. Based on the simulation results and previous work, I provide recommendations regarding which model selection methods should be adopted in different scenarios. The implications, limitations, and future research directions are also discussed.

Original languageEnglish (US)
Pages (from-to)7963-7984
Number of pages22
JournalBehavior Research Methods
Volume56
Issue number7
DOIs
StatePublished - Oct 2024

Bibliographical note

Publisher Copyright:
© This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2024.

Keywords

  • Count data
  • Generalized liner mixed models
  • Model selection
  • Monte Carlo simulation
  • Overdispersion
  • Single-case experimental design
  • Zero-inflation

PubMed: MeSH publication types

  • Journal Article

Fingerprint

Dive into the research topics of 'Model selection of GLMMs in the analysis of count data in single-case studies: A Monte Carlo simulation'. Together they form a unique fingerprint.

Cite this