Multilevel modeling in single-case studies with zero-inflated and overdispersed count data

Haoran Li, Wen Luo, Eunkyeng Baek

Research output: Contribution to journalArticlepeer-review

Abstract

Count outcomes are frequently encountered in single-case experimental designs (SCEDs). Generalized linear mixed models (GLMMs) have shown promise in handling overdispersed count data. However, the presence of excessive zeros in the baseline phase of SCEDs introduces a more complex issue known as zero-inflation, often overlooked by researchers. This study aimed to deal with zero-inflated and overdispersed count data within a multiple-baseline design (MBD) in single-case studies. It examined the performance of various GLMMs (Poisson, negative binomial [NB], zero-inflated Poisson [ZIP], and zero-inflated negative binomial [ZINB] models) in estimating treatment effects and generating inferential statistics. Additionally, a real example was used to demonstrate the analysis of zero-inflated and overdispersed count data. The simulation results indicated that the ZINB model provided accurate estimates for treatment effects, while the other three models yielded biased estimates. The inferential statistics obtained from the ZINB model were reliable when the baseline rate was low. However, when the data were overdispersed but not zero-inflated, both the ZINB and ZIP models exhibited poor performance in accurately estimating treatment effects. These findings contribute to our understanding of using GLMMs to handle zero-inflated and overdispersed count data in SCEDs. The implications, limitations, and future research directions are also discussed.

Original languageEnglish (US)
JournalBehavior Research Methods
DOIs
StateAccepted/In press - 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 linear mixed models
  • Monte Carlo simulation
  • Overdispersion
  • Single-case experimental design
  • Zero-inflation

PubMed: MeSH publication types

  • Journal Article

Fingerprint

Dive into the research topics of 'Multilevel modeling in single-case studies with zero-inflated and overdispersed count data'. Together they form a unique fingerprint.

Cite this