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Multilevel modeling in single-case studies with count and proportion data: A demonstration and evaluation.

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This study introduces generalized linear mixed models (GLMMs) for analyzing count and proportion data in single-case experimental designs (SCEDs). The research offers practical guidance and simulation-based recommendations for applying GLMMs effectively.

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Area of Science:

  • Statistics
  • Behavioral Research Methods

Background:

  • Single-case experimental designs (SCEDs) frequently yield count or proportion outcome data.
  • Existing statistical methods may not fully capture the complexities of such data.

Purpose of the Study:

  • To introduce and illustrate a new class of generalized linear mixed models (GLMMs) for analyzing count and proportion data in SCEDs.
  • To provide researchers with practical guidance on applying GLMMs, including aspects like overdispersion, estimation, and interpretation.

Main Methods:

  • Colloquial illustration of GLMMs for count and proportion data.
  • Detailed discussion of GLMM framework components: overdispersion, estimation, inference, model selection, and coefficient interpretation.
  • Empirical examples and simulation studies to evaluate GLMM performance.

Main Results:

  • Simulation studies assessed GLMM performance regarding biases and coverage rates for treatment effects (immediate and trend).
  • Empirical Type I error rates of statistical tests were examined.
  • Findings provide a basis for statistical decision-making when using GLMMs in SCED research.

Conclusions:

  • GLMMs offer a robust framework for analyzing count and proportion data in SCEDs.
  • The study provides essential information for SCED researchers and outlines future directions for methodologists.
  • Recommendations are offered for sound statistical decision-making when implementing GLMMs.