Titlebook: Explanatory Item Response Models; A Generalized Linear Paul Boeck,Mark Wilson Book 2004 Springer Science+Business Media New York 2004 SAS.p

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Steffen H?hne,Andreas B. Kilchertructures, such as multivariate observations, clustered data, repeated measurements (called ‘repeated observations’ in this volume), longitudinal data, and spatially correlated data. In particular, studies are often designed to investigate changes in a specific parameter which is measured repeatedly

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Beeinflussung der ?tzergebnissedictors vary both within and between persons. The inclusion of person-by-item predictors besides person predictors or item predictors is relevant for modeling various phenomena such as differential item functioning (DIF) and local item dependencies (LID) (see Zwinderman, 1997). To describe models wi

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Handbuch der viruswirksamen Desinfektionre discussed in Chapter 2. The Rasch model assumes item indicators as predictors, so that each item has a specific effect, the weight of the corresponding item indicator. The LLTM explains these effects in terms of item properties, or in other words item properties are used as item predictors. There

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Multiple person dimensions and latent item predictors with random person weights (following a normal distribution), and with no inclusion of person predictors (except for the constant). The extensions can be applied in much the same way to the other models that were discussed in the first two parts of this book.