New developments in categorical data analysis for the social and behavioral sciences / edited by L. Andries van der Ark, Marcel A. Croon, Klaas Sijtsma.

Includes bibliographical references and indexes.

Print version record.

Statistical models for categorical variables / L. Andries van der Ark, Marcel A. Croon, and Klaas Sijtsma -- Misclassification phenomena in categorical data analysis : regression toward the mean and tendency toward the mode / Jacques A. Hagenaars -- Factor analysis with categorical indicators : a comparison between traditional and latent class approaches / Jeroen K. Vermunt and Jay Magidson -- Bayesian computational methods for inequality constrained latent class analysis / Olav Laudy, Jan Boom, and Herbert Hoijtink -- Analyzing categorical data by marginal models / Wicher P. Bergsma and Marcel A. Croon -- Computational aspects of the E-M and Bayesian estimation in latent variable models / Irini Moustaki and Martin Knott -- Logistic models for single-subject time series / Peter W. van Rijn and Peter C.M. Molenaar -- The effect of missing data imputation on Mokken scale analysis / L. Andrew van der Ark and Klaas Sijtsma -- Building IRT models from scratch : graphical models, exchangeability, marginal freedom, scale types, and latent traits / Henk Kelderman -- The Nedlesky model for multiple-choice items / Timo M. Bechger [and others] -- Application of the polytomous saltus model to stage-like proportional reasoning data / Karen Draney and Mark Wilson -- Multilevel IRT model assessment / Jean-Paul Fox.

A collection of studies on modern categorical data analysis methods, emphasizing their application to relevant and interesting datasets. A prominent breakthrough in categorical data analysis is the development and use of latent variable models.

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