This course lays out the conceptual and statistical foundations of univariate and multivariate generalizability theory. It also provides a detailed coverage of advanced topics in univariate and multivariate generalizability theory. Beginning with single-facet designs, the course progresses through multi-facet universes and G-study designs and random effects D-study designs. Both crossed and nested designs are presented. Advanced topics covered include D-study procedures for situations involving restricted universes of generalization and sampling from finite universes; effects of hidden facets, stratified objects of measurement, cautions regarding reliability of groups means, conditional standard errors of measurement, universe score estimation, and comparison of generalizability theory with other measurement theories. Lab sessions will have students develop data analytic skills using the generalizability theory computer programs, such as GENOVA, urGENOVA, and mGENOVA.
Briesch, A. M., Swaminathan, H., Welsh, M., & Chafouleas, S. M. (2014). Generalizability theory: A practical guide to study design, implementation, and interpretation. Journal of School Psychology, 52(1), 13-35.