Multivariate generalizability theory (GT) represents a comprehensive framework for
quantifying score consistency, separating multiple sources contributing to measurement …

We demonstrate how to analyze complete multivariate generalizability theory (GT) designs
within structural equation modeling frameworks that encompass both individual subscale …

Generalizability theory provides a comprehensive framework for determining how multiple
sources of measurement error affect scores from psychological assessments and using that …

We illustrate how to analyze complete generalizability theory (GT) designs using structural
equation modeling software (lavaan in R), compare results to those obtained from numerous …

Although generalizability theory (GT) designs typically are analyzed using analysis of
variance (ANOVA) procedures, they also can be integrated into structural equation models …

Over the last decade, applications of bifactor modeling within clinical settings have
increased markedly but typically rely on data collected on single occasions. A shortcoming …

We demonstrate how to use structural equation models to represent generalizability theory-
based univariate, multivariate, and bifactor model designs. Analyses encompassed multi …

In recent years, researchers have described how to analyze generalizability theory (GT)
based univariate, multivariate, and bifactor designs using structural equation models …

Background Generalizability theory and bifactor models continue to play significant roles in
representing psychometric properties of scores from measures within a wide variety of …

We illustrate how structural equation models (SEMs) can be used to assess the reliability
and generalizability of composite and subscale scores, proportions of multiple sources of …