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• W2892638968 abstract "Abstract. Extending from classical test theory, G theory allows more sources of variations to be investigated and therefore provides the accuracy of generalizing observed scores to a broader universe. However, G theory has been used less due to the absence of analytic facilities for this purpose in popular statistical software packages. Besides, there is rarely a systematic G theory introduction in the linear mixed-effect model context, which is a widely taught technique in statistical analysis curricula. The present paper fits G theory into linear mixed-effect models and estimates the variance components via the well-known lme4 package in R. Concrete examples, modeling procedures, and R syntax are illustrated so that practitioners may use G theory for their studies. Realizing the G theory estimation in R provides more flexible features than other platforms, such that users need not rely on specialized software such as GENOVA and urGENOVA." @default.
• W2892638968 created "2018-10-05" @default.
• W2892638968 creator A5063837214 @default.
• W2892638968 date "2018-07-01" @default.
• W2892638968 modified "2023-10-10" @default.
• W2892638968 title "Using the Linear Mixed-Effect Model Framework to Estimate Generalizability Variance Components in R" @default.
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• W2892638968 doi "https://doi.org/10.1027/1614-2241/a000149" @default.
• W2892638968 hasPublicationYear "2018" @default.
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