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Regression Models for Categorical, Count, and Related Variables - An Applied Approach (Paperback)
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Regression Models for Categorical, Count, and Related Variables - An Applied Approach (Paperback)
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Social science and behavioral science students and researchers are
often confronted with data that are categorical, count a
phenomenon, or have been collected over time. Sociologists
examining the likelihood of interracial marriage, political
scientists studying voting behavior, criminologists counting the
number of offenses people commit, health scientists studying the
number of suicides across neighborhoods, and psychologists modeling
mental health treatment success are all interested in outcomes that
are not continuous. Instead, they must measure and analyze these
events and phenomena in a discrete manner. This book provides an
introduction and overview of several statistical models designed
for these types of outcomes - all presented with the assumption
that the reader has only a good working knowledge of elementary
algebra and has taken introductory statistics and linear regression
analysis. Numerous examples from the social sciences demonstrate
the practical applications of these models. The chapters address
logistic and probit models, including those designed for ordinal
and nominal variables, regular and zero-inflated Poisson and
negative binomial models, event history models, models for
longitudinal data, multilevel models, and data reduction techniques
such as principal components and factor analysis. Each chapter
discusses how to utilize the models and test their assumptions with
the statistical software Stata, and also includes exercise sets so
readers can practice using these techniques. Appendices show how to
estimate the models in SAS, SPSS, and R; provide a review of
regression assumptions using simulations; and discuss missing data.
A companion website includes downloadable versions of all the data
sets used in the book.
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