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Logistic Regression Models for Ordinal Response Variables
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Logistic Regression Models for Ordinal Response Variables

Volume: 146
February 2006 | 120 pages | SAGE Publications, Inc
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Logistic Regression Models for Ordinal Response Variables provides applied researchers in the social, educational, and behavioural sciences with 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. SPSS and SAS are used for all examples; data and syntax are available from the author's website. The examples are drawn from an educational context, but applications to other fields of inquiry are noted, such as HIV prevention, behavior change, counseling psychology, social psychology, etc.). The level of the book is set for applied researchers who need to quickly understand the use and application of these kinds of ordinal regression models.

 
List of Tables and Figures
 
Series Editor’s Introduction
 
Acknowledgments
 
1. Introduction
Purpose of This Book

 
Software and Syntax

 
Organization of the Chapters

 
 
2. Context: Early Childhood Longitudinal Study
Overview of the Early Childhood Longitudinal Study

 
Practical Relevance of Ordinal Outcomes

 
Variables in the Models

 
 
3. Background: Logistic Regression
Overview of Logistic Regression

 
Assessing Model Fit

 
Interpreting the Model

 
Measures of Association

 
EXAMPLE 3.1: Logistic Regression

 
Comparing Results Across Statistical Programs

 
 
4. The Cumulative (Proportional) Odds Model for Ordinal Outcomes
Overview of the Cumulative Odds Model

 
EXAMPLE 4.1: Cumulative Odds Model With a Single Explanatory Variable

 
EXAMPLE 4.2: Full-Model Analysis of Cumulative Odds

 
Assumption of Proportional Odds and Linearity in the Logit

 
Alternatives to the Cumulative Odds Model

 
EXAMPLE 4.3: Partial Proportional Odds

 
 
5. The Continuation Ratio Model
Overview of the Continuation Ratio Model

 
Link Functions

 
Probabilities of Interest

 
Directionality of Responses and Formation of the Continuation Ratios

 
EXAMPLE 5.1: Continuation Ratio Model With Logit Link and Restructuring the Data

 
EXAMPLE 5.2: Continuation Ratio Model With Complementary Log-Log Link

 
Choice of Link and Equivalence of Two Clog-Log Models

 
Choice of Approach for Continuation Ratio Models

 
EXAMPLE 5.3: Full-Model Continuation Ratio Analyses for the ECLS-K Data

 
 
6. The Adjacent Categories Model
Overview of the Adjacent Categories Model

 
EXAMPLE 6.1: Gender-Only Model

 
EXAMPLE 6.2: Adjacent Categories Model With Two Explanatory Variables

 
EXAMPLE 6.3: Full Adjacent Categories Model Analysis

 
 
7. Conclusion
Considerations for Further Study

 
 
Notes
 
Appendix A: Chapter 3
 
Appendix B: Chapter 4
 
Appendix C: Chapter 5
 
Appendix D: Chapter 6
 
References
 
Index
 
About the Author

For instructors

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