Categorical 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 response theory leads to the identification of one or more ordinal or interval scales. In psychological and educational testing these scales are used for individual measurement of abilities and personality traits.
The focus of this volume is applied. After a method is explained, the potential of the method for analyzing categorical data is illustrated by means of a real data example to show how it can be used effectively for solving a real data problem. These methods are accessible to researchers not trained explicitly in applied statistics. This volume appeals to researchers and advanced students in the social and behavioral sciences, including social, developmental, organizational, clinical and health psychologists, sociologists, educational and marketing researchers, and political scientists. In addition, it is of interest to those who collect data on categorical variables and are faced with the problem of how to analyze such variables--among themselves or in relation to metric variables.
"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.
This series presents methodological techniques for investigators and students.
Each volume focuses on a specific method with the goal of providing an understanding and working knowledge of each method with a minimum of mathematical derivations.