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This summary is machine-generated.

Generalized linear mixed models (GLMMs) can analyze single-case experimental designs (SCEDs) with count data, even with autocorrelation. This study evaluates robust GLMMs and new models with autoregressive errors for accurate treatment effect estimation.

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AutocorrelationMonte Carlo simulationcount datageneralized liner mixed modelsoverdispersionsingle-case experimental design

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Area of Science:

  • Statistical modeling
  • Behavioral research methods

Background:

  • Single-case experimental designs (SCEDs) provide valuable treatment effect insights.
  • Autocorrelation in SCED data can violate assumptions of standard statistical models.
  • Generalized linear mixed models (GLMMs) are increasingly used for SCED count data.

Purpose of the Study:

  • To assess the robustness of existing GLMMs for autocorrelated SCED count data.
  • To evaluate novel GLMMs and linear mixed models (LMMs) incorporating autoregressive errors.
  • To provide guidance on analyzing SCED count data with serial dependency.

Main Methods:

  • Monte Carlo simulation study.
  • Evaluation of bias, coverage rates, and Type I error rates.
  • Analysis of real-world SCED count data.

Main Results:

  • Previously used GLMMs showed varying robustness to autocorrelation.
  • New GLMMs and LMMs with autoregressive structures demonstrated improved performance.
  • Recommendations for handling autocorrelation in SCED count data analysis were established.

Conclusions:

  • Autocorrelation must be addressed when analyzing SCED count data.
  • Models incorporating autoregressive errors offer a viable solution.
  • Further research is needed to refine methods for SCED data analysis.