van der Ark, L. Andries; Croon, Marcel A.; Sijtsma, Klaas

"New Developments in Categorical Data Analysis for the Social and Behavioral Sciences is a collection of up-to-date studies on modern categorical data analysis methods, emphasizing their application to relevant and interesting data sets. A prominent breakthrough in categorical data analysis are latent variable models. This volume concentrates on two classes of models -- latent class analysis and item response theory."—Short Book Reviews"The book is certainly beneficial and easily accessible to researchers and graduate students in the social and behavioral sciences."—American Statistical Association

• Contents: Preface. L.A. van der Ark, M.A. Croon, K. Sijtsma, Statistical Models for Categorical Variables. J.A. Hagenaars, Misclassification Phenomena in Categorical Data Analysis: Regression Toward the Mean and Tendency Toward the Mode. J.K. Vermunt, J. Magidson, Factor Analysis With Categorical Indicators: A Comparison Between Traditional and Latent Class Approaches. O. Laudy, J. Boom, H. Hoijtink, Bayesian Computational Methods for Inequality Constrained Latent Class Analysis. W.P. Bergsma, M.A. Croon, Analyzing Categorical Data by Marginal Models. I. Moustaki, M. Knott, Computational Aspects of the E-M and Bayesian Estimation in Latent Variable Models. P.W. van Rijn, P.C.M. Molenaar, Logistic Models for Single-Subject Time Series. L.A. van der Ark, K. Sijtsma, The Effect of Missing Data Imputation on Mokken Scale Analysis. H. Kelderman, Building IRT Models From Scratch: Graphical Models, Exchangeability, Marginal Freedom, Scale Types, and Latent Traits. T.M. Bechger, G. Maris, H.H.F.M. Verstralen, N.D. Verhelst, The Nedelsky Model For Multiple-Choice Items. K. Draney, M. Wilson, Application of the Polytomous Saltus Model to Stage-Like Proportional Reasoning Data. J-P. Fox, Multilevel IRT Model Assessment.

van der Ark, L. Andries; Croon, Marcel A.; Sijtsma, Klaas

"New Developments in Categorical Data Analysis for the Social and Behavioral Sciences is a collection of up-to-date studies on modern categorical data analysis methods, emphasizing their application to relevant and interesting data sets. A prominent breakthrough in categorical data analysis are latent variable models. This volume concentrates on two classes of models -- latent class analysis and item response theory."—Short Book Reviews"The book is certainly beneficial and easily accessible to researchers and graduate students in the social and behavioral sciences."—American Statistical Association

• Contents: Preface. L.A. van der Ark, M.A. Croon, K. Sijtsma, Statistical Models for Categorical Variables. J.A. Hagenaars, Misclassification Phenomena in Categorical Data Analysis: Regression Toward the Mean and Tendency Toward the Mode. J.K. Vermunt, J. Magidson, Factor Analysis With Categorical Indicators: A Comparison Between Traditional and Latent Class Approaches. O. Laudy, J. Boom, H. Hoijtink, Bayesian Computational Methods for Inequality Constrained Latent Class Analysis. W.P. Bergsma, M.A. Croon, Analyzing Categorical Data by Marginal Models. I. Moustaki, M. Knott, Computational Aspects of the E-M and Bayesian Estimation in Latent Variable Models. P.W. van Rijn, P.C.M. Molenaar, Logistic Models for Single-Subject Time Series. L.A. van der Ark, K. Sijtsma, The Effect of Missing Data Imputation on Mokken Scale Analysis. H. Kelderman, Building IRT Models From Scratch: Graphical Models, Exchangeability, Marginal Freedom, Scale Types, and Latent Traits. T.M. Bechger, G. Maris, H.H.F.M. Verstralen, N.D. Verhelst, The Nedelsky Model For Multiple-Choice Items. K. Draney, M. Wilson, Application of the Polytomous Saltus Model to Stage-Like Proportional Reasoning Data. J-P. Fox, Multilevel IRT Model Assessment.