Applications of Generalizability Theory and Their Relations to Classical Test Theory and Structural Equation Modeling Walter P. Vispoel, Carrie A. Morris, and Murat Kilinc University of Iowa Abstract Although widely recognized as a comprehensive framework for representing score reliability, general- izability theory (G-theory), despite its potential benefits, has been used sparingly in reporting of results for measures of individual differences. In this article, we highlight many valuable ways that G-theory can be used to quantify, evaluate, and improve psychometric properties of scores. Our illustrations encompass assessment of overall reliability, percentages of score variation accounted for by individual sources of measurement error, dependability of cut-scores for decision making, estimation of reliability and dependability for changes made to measurement procedures, disattenuation of validity coefficients for measurement error, and linkages of G-theory with classical test theory and structural equation modeling. We also identify computer packages for performing G-theory analyses, most of which can be obtained free of charge, and describe how they compare with regard to data input requirements, ease of use, complexity of designs supported, and output produced. Translational Abstract Generalizability theory (G-theory) is widely recognized as a comprehensive framework for representing score reliability. However, despite its potential benefits, G-theory has been used sparingly in reporting of results for measures of individual differences. In this article, we describe G-theory in a straightforward manner and highlight many valuable ways it can be used to quantify, evaluate, and improve psychometric properties of scores. Our illustrations encompass assessment of overall reliability, percentages of score variation accounted for by individual sources of measurement error, dependability of cut-scores for decision making, estimation of reliability and dependability for changes made to measurement proce- dures, disattenuation of validity coefficients for measurement error, and linkages of G-theory with classical test theory and structural equation modeling. We also identify computer packages for perform- ing G-theory analyses, most of which can be obtained free of charge, and describe how they compare with regard to data input requirements, ease of use, complexity of designs supported, and output produced. These resources, along with formulas provided throughout the article, should enable readers to apply G-theory to their own research and understand how it aligns with and differs from other measurement models. Keywords: generalizability theory, reliability, validity, classical test theory, structural equation modeling Over 40 years have passed since Cronbach, Gleser, Nanda, and Rajaratnam (1972) published their seminal treatise on gen- eralizability theory (G-theory)—The Dependability of Behav- ioral Measurements: Theory of Generalizability for Scores and Profiles. Their work significantly broadened perspectives on measurement theory by providing a comprehensive framework for estimating score consistency with reference to multiple sources of measurement error. Over time, many additional treatments of G-theory have appeared that summarize and ex- pand the work of Cronbach et al. (see, e.g., Brennan, 2001a; Crocker & Algina, 1986; Feldt & Brennan, 1989; Haertel, 2006; Marcoulides, 2000; Raykov & Marcoulides, 2011; Shavelson & Webb, 1991; Shavelson, Webb, & Rowley, 1989; Wiley, Webb, & Shavelson, 2013). Yet despite the strong interest in G-theory within the measurement community, applications of it are still rare when reporting results for measures of individual differ- ences. Possible reasons for such neglect may be G-theory’s technical vocabulary, overlooked linkages between it and clas- sical test theory (CTT), and difficulty in finding and running software for doing G-theory analyses. The purpose of this article is to describe G-theory in a straightforward manner, illustrate effective ways it can be used with measures of indi- vidual differences, highlight many of its direct connections with conventional indices of reliability and validity, show how G-theory can be approached from a structural equation model- ing perspective, and identify computer resources for conducting G-theory analyses. This article was published Online First January 23, 2017. Walter P. Vispoel, Carrie A. Morris, and Murat Kilinc, Department of Psychological and Quantitative Foundations, University of Iowa. We thank Patricia Martin for her help in preparing and proof reading drafts of the submitted manuscript. Correspondence concerning this article should be addressed to Walter P. Vispoel, Department of Psychological and Quantitative Foundations, Uni- versity of Iowa, 361 Lindquist Center, Iowa City, IA 52242-1529. E-mail: walter-vispoel@uiowa.edu This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. Psychological Methods © 2017 American Psychological Association 2018, Vol. 23, No. 1, 1–26 1082-989X/18/$12.00 http://dx.doi.org/10.1037/met0000107 1