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

Generalized linear mixed models (GLMMs) have great potential to deal with count and rate data in single-case experimental designs (SCEDs). However, applied researchers face challenges to apply such an advanced approach in their own studies. Hence, our study aimed to provide a tutorial and demonstrate a step-by-step procedure of using GLMMs to handle SCED count and rate outcomes.

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

The outcomes in single-case experimental designs (SCEDs) are often counts or proportions. In our study, we provided a colloquial illustration for a new class of generalized linear mixed models (GLMMs) to fit count and proportion data from SCEDs. We also addressed important aspects in the GLMM framework including overdispersion, estimation methods, statistical inferences, model selection methods by detecting overdispersion, and interpretations of regression coefficients.

Meta-Analysis of Single-Case Experimental Design using Multilevel Modeling.

Multilevel modeling (MLM) is an approach for meta-analyzing single-case experimental designs (SCED). In this paper, we provide a step-by-step guideline for using the MLM to meta-analyze SCED time-series data. The MLM approach is first presented using a basic three-level model, then gradually extended to represent more realistic situations of SCED data, such as modeling a time variable, moderators representing different design types and multiple outcomes, and heterogeneous within-case variance.

Estimation and statistical inferences of variance components in the analysis of single-case experimental design using multilevel modeling.

Multilevel models (MLMs) can be used to examine treatment heterogeneity in single-case experimental designs (SCEDs). With small sample sizes, common issues for estimating between-case variance components in MLMs include nonpositive definite matrix, biased estimates, misspecification of covariance structures, and invalid Wald tests for variance components with bounded distributions. To address these issues, unconstrained optimization, model selection procedure based on parametric bootstrap, and restricted likelihood ratio test (RLRT)-based procedure are introduced.