Logistic Regression Models for Ordinal Response Variables

Ann A. O'Connell - Ohio State University, USA

Volume: 146

Series:
Quantitative Applications in the Social Sciences

November 2005 | 120 pages | SAGE Publications, Inc

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. 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.

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ISBN: 9780761929895	Paperback	Suggested Retail Price: $46.00	Bookstore Price: $36.80
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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

Key features

Explores model fit statistics and provides information on how to run these models within the major statistics packages
Provides comparative interpretations among the models using current data from the Early Childhood Longitudinal Study (ECLS) to provide worked out examples of the concepts
Gives an example of the cumulative odds model within a multilevel context (children within schools)
Provides worked out examples from public health, education, management, and psychology

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Logistic Regression Models for Ordinal Response Variables

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