Del 146 - Quantitative Applications in the Social Sciences

Logistic Regression Models for Ordinal Response Variables

Häftad, Engelska, 2006

AvAnn Aileen O'Connell,Ann Aileen O′Connell,Ann A. O'Connell

929 kr

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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.

Produktinformation

Utgivningsdatum2006-02-02
Mått139 x 215 x 7 mm
Vikt170 g
FormatHäftad
SpråkEngelska
SerieQuantitative Applications in the Social Sciences
Antal sidor120
Upplaga1
FörlagSAGE Publications
ISBN9780761929895

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List of Tables and FiguresSeries Editor’s IntroductionAcknowledgments1. IntroductionPurpose of This BookSoftware and SyntaxOrganization of the Chapters2. Context: Early Childhood Longitudinal StudyOverview of the Early Childhood Longitudinal StudyPractical Relevance of Ordinal OutcomesVariables in the Models3. Background: Logistic RegressionOverview of Logistic RegressionAssessing Model FitInterpreting the ModelMeasures of AssociationEXAMPLE 3.1: Logistic RegressionComparing Results Across Statistical Programs4. The Cumulative (Proportional) Odds Model for Ordinal OutcomesOverview of the Cumulative Odds ModelEXAMPLE 4.1: Cumulative Odds Model With a Single Explanatory VariableEXAMPLE 4.2: Full-Model Analysis of Cumulative OddsAssumption of Proportional Odds and Linearity in the LogitAlternatives to the Cumulative Odds ModelEXAMPLE 4.3: Partial Proportional Odds5. The Continuation Ratio ModelOverview of the Continuation Ratio ModelLink FunctionsProbabilities of InterestDirectionality of Responses and Formation of the Continuation RatiosEXAMPLE 5.1: Continuation Ratio Model With Logit Link and Restructuring the DataEXAMPLE 5.2: Continuation Ratio Model With Complementary Log-Log LinkChoice of Link and Equivalence of Two Clog-Log ModelsChoice of Approach for Continuation Ratio ModelsEXAMPLE 5.3: Full-Model Continuation Ratio Analyses for the ECLS-K Data6. The Adjacent Categories ModelOverview of the Adjacent Categories ModelEXAMPLE 6.1: Gender-Only ModelEXAMPLE 6.2: Adjacent Categories Model With Two Explanatory VariablesEXAMPLE 6.3: Full Adjacent Categories Model Analysis7. ConclusionConsiderations for Further StudyNotesAppendix A: Chapter 3Appendix B: Chapter 4Appendix C: Chapter 5Appendix D: Chapter 6ReferencesIndexAbout the Author