Quantitative Methodology

Almost all research in the social and behavioral sciences, economics, marketing, criminology, and medicine deals with the analysis of categorical data. Categorical data are quantified as either nominal or ordinal variables. Nominal variables are used to distinguish between different groups, such as by gender, socio-economic status, education, religion, and political persuasion. Different scores on ordinal variables distinguish levels of interest, such as the choice of the preferred politician for President, the preferred type of punishment for committing burgulary, etc. 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 datasets. A prominent breakthrough in categorical data analysis is the development and use of latent variable models. This volume concentrates on two such classes of models-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 most likely pattern of scores on the categorical variables. This provides insight into the mechanisms producing the data, as well as the estimation of factor structures and regression models conditional on the latent class structure. 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. The methods are explained at a level that is accessible to researchers not trained explicitly in applied statistics. This volume will appeal 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 will be 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.