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Ordered Regression Models - Parallel, Partial, and Non-Parallel Alternatives (Paperback)
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Ordered Regression Models - Parallel, Partial, and Non-Parallel Alternatives (Paperback)
Series: Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences
Expected to ship within 10 - 15 working days
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Estimate and Interpret Results from Ordered Regression Models
Ordered Regression Models: Parallel, Partial, and Non-Parallel
Alternatives presents regression models for ordinal outcomes, which
are variables that have ordered categories but unknown spacing
between the categories. The book provides comprehensive coverage of
the three major classes of ordered regression models (cumulative,
stage, and adjacent) as well as variations based on the application
of the parallel regression assumption. The authors first introduce
the three "parallel" ordered regression models before covering
unconstrained partial, constrained partial, and nonparallel models.
They then review existing tests for the parallel regression
assumption, propose new variations of several tests, and discuss
important practical concerns related to tests of the parallel
regression assumption. The book also describes extensions of
ordered regression models, including heterogeneous choice models,
multilevel ordered models, and the Bayesian approach to ordered
regression models. Some chapters include brief examples using Stata
and R. This book offers a conceptual framework for understanding
ordered regression models based on the probability of interest and
the application of the parallel regression assumption. It
demonstrates the usefulness of numerous modeling alternatives,
showing you how to select the most appropriate model given the type
of ordinal outcome and restrictiveness of the parallel assumption
for each variable. Web ResourceMore detailed examples are available
on a supplementary website. The site also contains JAGS, R, and
Stata codes to estimate the models along with syntax to reproduce
the results.
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