Ordered Regression Models: Parallel, Partial, and Non-Parallel Alternatives - Andrew S Fullerton,Jun Xu
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This 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. It explores the advantages of ordered regression models over linear and binary regression models for the analysis of ordinal outcomes. The book also highlights several ways to interpret and pr ... Full description
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Description
This 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. It explores the advantages of ordered regression models over linear and binary regression models for the analysis of ordinal outcomes. The book also highlights several ways to interpret and present the results by using empirical examples from the social and behavioral sciences.
Includes detailed examples and code online
More Information
| Author | Andrew S Fullerton, Jun Xu |
|---|---|
| Publisher | CRC Press |
| Release year | 2016 |
| Cover type | Hardcover |
| EAN | 9781466569737 |