Logistic Regression Models for Ordinal Response Variables

Logistic Regression Models for Ordinal Response Variables provides applied researchers in the social, educational, and behavioral sciences with an accessible and comprehensive coverage of analyses for ordinal outcomes. The content builds on a review of logistic regression, and extends to details of the cumulative (proportional) odds, continuation ratio, and adjacent category models for ordinal data. Description and examples of partial proportional odds models are also provided. This book is highly readable, with lots of examples and in-depth explanations and interpretations of model characteristics.

This pocket guide familiarizes applied researchers, particularly those within education and social and behavioral sciences, with alternatives for the analysis of ordinal response variables that are faithful to the actual level of measure of the outcome. Using an early childhood longitudinal study it gives background on logistic regression, then covers the cumulative (proportional) odds model for ordinal outcomes, the continuation ratio model and the adjacent categories model with a number of examples and consideration of SPSS, SAS and SPSS PLUM as tools. It also includes considerations for further study. Annotation ©2006 Book News, Inc., Portland, OR

Scholarly Interests

Statistical methods, with an emphasis on single and multilevel generalized linear models; evaluation of professional development and health/education programs or interventions, particularly for HIV prevention; secondary analysis of large-scale databases; capacity building for community-based organizations; translation of evidence-based interventions.