Description
Book SynopsisEstimate 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
Trade Review
"The book is intended to be a starter for somebody not familiar with the subject. It was written primarily for social scientists (published in the CRC Statistics in the Social and Behavioral Sciences Series) and as such, it can be read easily without any statistical pre-requisites beyond very basic Statistics and some working knowledge of logistic regression. Nevertheless, the book is certainly useful far beyond the social sciences themselves – in particular for epidemiologists, medical researchers and also statisticians of students of Statistics/Biostatistics who want to learn basic facts about ordered regression and perhaps motivate further study of this interesting field. The style of exposition is quite informal and intuitive."
~International Society for Clinical Biostatistics
Table of ContentsIntroduction. Parallel Models. Partial Models. Nonparallel Models. Testing the Parallel Regression Assumption. Extensions. References. Index.
