Ordered Regression Models

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A01=Andrew Fullerton
A01=Andrew S. Fullerton
A01=Jun Xu
adjacent
Adjacent Category Logit Model
Adjacent Category Model
Adjacent Models
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Age Group_Uncategorized
assumption
Author_Andrew Fullerton
Author_Andrew S. Fullerton
Author_Jun Xu
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Average Marginal Effects
Binary Regression Models
Brant Test
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Category=JMB
Category=PBT
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complementary
Complementary Log Log
Complementary Log Log Model
Constrained Partial
continuation
Continuation Ratio
Continuation Ratio Logit Model
Continuation Ratio Models
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Cumulative Logit
Cumulative Logit Model
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General Self-rated Health
health
Heterogeneous Choice Model
Highest Density Intervals
IIA Assumption
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logit
LR Test
Nonlinear Probability Models
Ordered Regression Models
Ordinal Outcomes
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parallel
Parallel Assumption
Parallel Regression Assumption
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Product details

  • ISBN 9781466569737
  • Weight: 498g
  • Dimensions: 178 x 254mm
  • Publication Date: 05 Apr 2016
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
  • Language: English
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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.

Andrew S. Fullerton is an associate professor of sociology at Oklahoma State University. His primary research interests include work and occupations, social stratification, and quantitative methods. His work has been published in journals such as Social Forces, Social Problems, Sociological Methods & Research, Public Opinion Quarterly, and Social Science Research.

Jun Xu is an associate professor of sociology at Ball State University. His primary research interests include Asia and Asian Americans, social epidemiology, and statistical modeling and programing. His work has been published in journals such as Social Forces, Social Science & Medicine, Sociological Methods & Research, Social Science Research, and The Stata Journal.