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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
1University of Minnesota, USA.
Evaluation & the Health Professions
|December 11, 2024
Summary
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.
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.
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