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Using Generalized Linear Mixed Models in the Analysis of Count and Rate Data in Single-case Eperimental Designs: A

Haoran Li1, Eunkyeng Baek2, Wen Luo2

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Summary
This summary is machine-generated.

Generalized linear mixed models (GLMMs) offer advanced analysis for single-case experimental designs (SCEDs). This tutorial demonstrates using GLMMs for SCED count and rate data, supporting prelinguistic milieu teaching effectiveness in children with autism.

Keywords:
autismcount and rate outcomesempirical demonstrationgeneralized linear mixed modelssingle-case experimental designtutorial

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Area of Science:

  • Behavioral Science
  • Statistical Modeling
  • Developmental Psychology

Background:

  • Single-case experimental designs (SCEDs) generate valuable data but often require advanced statistical methods.
  • Generalized linear mixed models (GLMMs) are powerful tools for analyzing count and rate data common in SCEDs.
  • Applied researchers may find implementing GLMMs challenging, necessitating practical guidance.

Purpose of the Study:

  • To provide a tutorial on applying generalized linear mixed models (GLMMs) to single-case experimental design (SCED) data.
  • To demonstrate a step-by-step procedure for analyzing count and rate outcomes using GLMMs.
  • To illustrate the application of GLMMs using an empirical example examining prelinguistic milieu teaching (PMT).

Main Methods:

  • Utilized an empirical dataset from six school-age children with autism receiving prelinguistic milieu teaching (PMT).
  • Analyzed outcomes of sustained intentional communication (frequency count) and initiated intentional communication (rate) using GLMMs.
  • Provided associated R and SAS code for the step-by-step analytical procedure.

Main Results:

  • GLMM analysis supported the original findings on the effectiveness of prelinguistic milieu teaching (PMT).
  • GLMMs provided precise estimates of individual treatment effects and between-case variation.
  • Interpreted similarities and differences between GLMM findings and the original study's conclusions.

Conclusions:

  • GLMMs offer a robust method for analyzing count and rate data in SCEDs, enhancing understanding of treatment effects.
  • The application of GLMMs in this study confirmed the effectiveness of PMT for improving prelinguistic communication in children with autism.
  • This work provides practical guidance and code for researchers to implement advanced statistical analyses in SCED research.