Accessible 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.
Cuprins
Concepts in Generalizability Theory
Statistical Model Underlying Generalizability Theory
Generalizability Studies with Crossed Facets
Generalizability Studies with Nested Facets
Generalizability Studies with Fixed Facets
Decision Studies
Measurement Error and Generalizability Coefficients
Generalizability and Decision Studies with the Same Design
Generalizability and Decision Studies with Different Designs
Summary and Next Steps
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