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The Generalizability Theory -- Cronbach et al. 1972

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The Generalizability Theory -- Cronbach et al. 1972

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    6. The Generalizability Theory -- Cronbach et al. (1972) The theory effectively demonstrates that measurement error is multifaceted. Using the G theory, we may conceptualize the longitudinal rating data collected by multiple raters as a two-facet design (that is, rater and occasion) with study subjects as the object of measurement.

    23. Important References (1) Laird & Ware (1982). Random-effects models for longitudinal data. Biometrics, 38, 964-974. Bryk & Raudenbush (1992). Hierarchical linear models: Applications and data analysis methods. Sage Publications. Diggle, Liang, & Zeger (1995). Analysis of longitudinal data. Oxford: Clarendon Press. Littell, Milliken, Stroup, Wolfinger (1996). SAS system for mixed models. Cary: SAS Institute, Inc.

    24. Important References (2) Verbeke & Molenberghs (2000). Linear mixed models for longitudinal data. Springer. Little, Schnabel, & Baumert (2000). Modeling longitudinal and multilevel data. Lawrence Erlbaum Associates, Publishers. Sampson, Raudenbush, & Earls (1997). Neighborhoods and violent crime: A multilevel study of collective efficacy. Science 277, 918-924.

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