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 healthpsychologists,
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.
General
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