Generalizability theory offers an extensive conceptual framework and a powerful set of statistical procedures for characterizing and quantifying the fallibility of measurements. Robert Brennan, the author, has written the most comprehensive and up-to-date treatment of generalizability theory. The book provides a synthesis of those parts of the statistical literature that are directly applicable to generalizability theory. The principal intended audience is measurement practitioners and graduate students in the behavioral and social sciences, although a few examples and references are provided from other fields. Readers will benefit from some familiarity with classical test theory and analysis of variance, but the treatment of most topics does not presume specific background.

Inhalt

Introduction Single-facet Designs Multifacet Universes of Admissible Observations and G Study Designs Multifacet Universes of Generalization and D Study Designs Advanced Topics in Univariate Generalizability Theory Variability of Statistics in Generalizability Theory Unbalanced Random Effects Designs Unbalanced Random Effects Designs--Examples Multivariate G Studies Multivariate D Studies Multivariate Regressed Scores