Logistic regression : from introductory to advanced concepts and applications

著者

書誌事項

Logistic regression : from introductory to advanced concepts and applications

Scott Menard

Sage, c2010

  • : hardcover

大学図書館所蔵 件 / 全8件

この図書・雑誌をさがす

注記

Bibliography: p. 358-368

Includes indexes

内容説明・目次

内容説明

In this text, author Scott Menard provides coverage of not only the basic logistic regression model but also advanced topics found in no other logistic regression text. The book keeps mathematical notation to a minimum, making it accessible to those with more limited statistics backgrounds, while including advanced topics of interest to more statistically sophisticated readers. Not dependent on any one software package, the book discusses limitations to existing software packages and ways to overcome them. Key Features Examines the logistic regression model in detail Illustrates concepts with applied examples to help readers understand how concepts are translated into the logistic regression model Helps readers make decisions about the criteria for evaluating logistic regression models through detailed coverage of how to assess overall models and individual predictors for categorical dependent variables Offers unique coverage of path analysis with logistic regression that shows readers how to examine both direct and indirect effects using logistic regression analysis Applies logistic regression analysis to longitudinal panel data, helping students understand the issues in measuring change with dichotomous, nominal, and ordinal dependent variables Shows readers how multilevel change models with logistic regression are different from multilevel growth curve models for continuous interval or ratio-scaled dependent variables Logistic Regression is intended for courses such as Regression and Correlation, Intermediate/Advanced Statistics, and Quantitative Methods taught in departments throughout the behavioral, health, mathematical, and social sciences, including applied mathematics/statistics, biostatistics, criminology/criminal justice, education, political science, public health/epidemiology, psychology, and sociology.

目次

Preface Chapter 1. Introduction: Linear Regression and Logistic Regression Chapter 2. Log-Linear Analysis, Logit Analysis, and Logistic Regression Chapter 3. Quantitative Approaches to Model Fit and Explained Variation Chapter 4. Prediction Tables and Qualitative Approaches to Explained Variation Chapter 5. Logistic Regression Coefficients Chapter 6. Model Specification, Variable Selection, and Model Building Chapter 7. Logistic Regression Diagnostics and Problems of Inference Chapter 8. Path Analysis With Logistic Regression (PALR) Chapter 9. Polytomous Logistic Regression for Unordered Categorical Variables Chapter 10. Ordinal Logistic Regression Chapter 11. Clusters, Contexts, and Dependent Data: Logistic Regression for Clustered Sample Survey Data Chapter 12. Conditional Logistic Regression Models for Related Samples Chapter 13. Longitudinal Panel Analysis With Logistic Regression Chapter 14. Logistic Regression for Historical and Developmental Change Models: Multilevel Logistic Regression and Discrete Time Event History Analysis Chapter 15. Comparisons: Logistic Regression and Alternative Models Appendix A: ESTIMATION FOR LOGISTIC REGRESSION MODELS Appendix B: PROOFS RELATED TO INDICES OF PREDICTIVE EFFICIENCY Appendix C: ORDINAL MEASURES OF EXPLAINED VARIATION References Index

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