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Statistical Models for the Social and Behavioral Sciences - Multiple Regression and Limited-Dependent Variable Models (Hardcover)
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Statistical Models for the Social and Behavioral Sciences - Multiple Regression and Limited-Dependent Variable Models (Hardcover)
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Multiple regression analysis has been widely used by researchers to
analyze complex social problems since the 1950s. A specialization
in economics, known as econometrics, developed out of a recognition
that multiple regression is based upon a large number of
assumptions-many of which are commonly violated in specific
applications. Econometricians developed tests for violations of the
regression model assumptions, as well as a variety of corrective
measures for estimating regression models in the presence of many
of the violations. Unfortunately, the mathematical sophistication
required to understand the econometrics literature started out high
and has continued to rise over the years. As a consequence, an
understanding of the assumptions of the regression model, tests for
violations, and corrective estimation approaches have failed to
permeate widely many other policy-related disciplines such as
political science, social work, public administration, and
sociology. One of the key objectives of this book is to translate
the results from the econometrics literature into language that
policy analysts from other disciplines can understand easily. A
second objective is to present a discussion of so-called
limited-dependent variable models. One of the assumptions of the
regression model is that the dependent variable is measured on an
interval scale. But often the dependent variable of interest is
discrete or categorical. Whether someone is in poverty or, whether
they are working full-time, part-time, or out of the labor force,
marital status-all are examples of categorical variables that might
be of policy interest. Moreover, the growing availability of
large-scale public use data sets containing information on
individuals and families has heightened the relevance of
categorical variables in policy analysis. The mathematical
preparation required to understand procedures for estimating
categorical models is, however, even more daunting than that for
fully understanding and using the regression model. As with the
theoretical development of the regression model, most presentations
of categorical models, such as Logit and Probit, are to be found in
econometric literature. Moreover, this literature offers little in
the way of practical advice on how to estimate and interpret model
results. This book is the first to present a detailed and
accessible discussion of multiple regression and limited-dependent
variable models in the context of policy analysis. As such it will
be an invaluable resource for most scholars, researchers, and
students in the social and behavioral sciences.
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