Description
Book SynopsisCategorical data are quantified as either nominal variables--distinguishing different groups, for example, based on socio-economic status, education, and political persuasion--or ordinal variables--distinguishing levels of interest, such as the preferred politician or the preferred type of punishment for committing burglary. This new book is a collection of up-to-date studies on modern categorical data analysis methods, emphasizing their application to relevant and interesting data sets.
This volume concentrates on latent class analysis and item response theory. These methods use latent variables to explain the relationships among observed categorical variables. Latent class analysis yields the classification of a group of respondents according to their pattern of scores on the categorical variables. This provides insight into the mechanisms producing the data and allows the estimation of factor structures and regression models conditional on the latent class structure. Item res
Trade Review"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
Table of ContentsContents: 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.
