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

Haoran Li1, Wen Luo2, Eunkyeng Baek2

  • 1Department of Educational Psychology, University of Minnesota, Minneapolis, MN, USA. haoranli@umn.edu.

Behavior Research Methods
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Summary
This summary is machine-generated.

The zero-inflated negative binomial (ZINB) model accurately estimates treatment effects in single-case experimental designs (SCEDs) with zero-inflated count data. Other models showed biased results, highlighting ZINB

Keywords:
Count dataGeneralized linear mixed modelsMonte Carlo simulationOverdispersionSingle-case experimental designZero-inflation

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Area of Science:

  • Behavioral Science
  • Statistical Modeling
  • Psychometrics

Background:

  • Count outcomes are common in single-case experimental designs (SCEDs).
  • Generalized linear mixed models (GLMMs) can handle overdispersed count data.
  • Zero-inflation in baseline data of SCEDs presents a significant analytical challenge.

Purpose of the Study:

  • To address zero-inflated and overdispersed count data in SCEDs using a multiple-baseline design (MBD).
  • To evaluate the performance of various GLMMs (Poisson, NB, ZIP, ZINB) for estimating treatment effects and inferential statistics in SCEDs.
  • To demonstrate the analysis of such data with a real-world example.

Main Methods:

  • Simulated zero-inflated and overdispersed count data within an MBD framework.
  • Applied four GLMMs: Poisson, negative binomial (NB), zero-inflated Poisson (ZIP), and zero-inflated negative binomial (ZINB).
  • Assessed the accuracy of treatment effect estimates and the reliability of inferential statistics.

Main Results:

  • The ZINB model provided accurate treatment effect estimates for zero-inflated and overdispersed data.
  • Poisson, NB, and ZIP models yielded biased estimates when data were zero-inflated.
  • ZINB and ZIP models performed poorly when data were overdispersed but not zero-inflated.

Conclusions:

  • The ZINB model is recommended for analyzing zero-inflated and overdispersed count data in SCEDs with MBDs.
  • Researchers should carefully consider the presence of zero-inflation and overdispersion when selecting statistical models for SCEDs.
  • Further research is needed to explore alternative methods and address limitations in handling complex count data structures in SCEDs.