Generalizability Theory: A PrimerAccessible to any professional or researcher who has a basic understanding of analysis of variance, Shavelson and Webb offer an intuitive development of generalizability theory, a technique for estimating the relative magnitudes of various components of error variation and for indicating the most efficient strategy for achieving desired measurement precision. Covering a variety of topics such as generalizability studies with nested facets and with fixed facets, measurement error and generalizability coefficients, and decision studies with same and with different designs, the text includes exercises so the reader may practice the application of each chapter's material. By using detailed illustrations and examples, Shavelson and Webb clearly describe the logic underlying major concepts in generalizability theory to enable readers to apply these methods when investigating the consistency of their own measurements. |
Contents
Concepts in Generalizability Theory | 1 |
Statistical Model Underlying Generalizability Theory | 17 |
Generalizability Studies with Crossed Facets | 27 |
Generalizability Studies with Nested Facets | 46 |
Generalizability Studies with Fixed Facets | 65 |
Measurement Error | 83 |
Generalizability and Decision Studies with | 99 |
Generalizability and Decision Studies with Different | 115 |
Summary and Next Steps | 127 |
Other editions - View all
Common terms and phrases
absolute decisions absolute error variance analysis of variance ANOVA average chapter classical test theory component for items confounded contribute to error crossed design D-study designs decision maker Decision Studies estimated generalizability estimated variance components evaluated example expected mean square fixed facet fully random G coefficient G theory generalizability coefficients generalizability theory grand mean increasing the number interaction interpretations item effect magnitude mean square equations measurement error negative estimate nested design nested facets number of conditions number of items number of raters object of measurement observed score one-facet design opie partially nested person-by-item phi coefficients raters and occasions relative and absolute relative decisions relative standing residual variance scale self-concept separately Shavelson sources of error sources of variation standing of persons subject matters subtests Table teacher behavior total variance two-facet design universe of admissible universe score universe-score variance variability Venn diagrams Xpij