Digital Module 39: Introduction to Generalizability Theory

Module Abstract Generalizability theory (GT) is a widely used framework in the social and behavioral sciences for assessing the reliability of measurements. Unlike classical test theory, which treats measurement error as a single undifferentiated term, GT enables the decomposition of error into multiple distinct components. This module introduces the core principles and applications of GT, with a focus on the univariate framework. The first four sections cover foundational concepts, including key terminology, common design structures, and the estimation of variance components. The final two sections offer hands‐on examples using real data, implemented in R and GENOVA software. By the end of the module, participants will have a solid understanding of GT and the ability to conduct basic GT analyses using statistical software.