Logistic Regression Models for Ordinal Response Variables

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