Logistic Regression

From Introductory to Advanced Concepts and Applications
Scott Menard - Sam Houston State University, USA
Logistic Regression
April 2009 | 392 pages | Sage US
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Description

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.



Contents

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 A: ESTIMATION FOR LOGISTIC REGRESSION MODELS

Appendix B: PROOFS RELATED TO INDICES OF PREDICTIVE EFFICIENCY

Appendix B: PROOFS RELATED TO INDICES OF PREDICTIVE EFFICIENCY

Appendix C: ORDINAL MEASURES OF EXPLAINED VARIATION

Appendix C: ORDINAL MEASURES OF EXPLAINED VARIATION

References

References

Index

Index

Description

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.



Contents

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 A: ESTIMATION FOR LOGISTIC REGRESSION MODELS

Appendix B: PROOFS RELATED TO INDICES OF PREDICTIVE EFFICIENCY

Appendix B: PROOFS RELATED TO INDICES OF PREDICTIVE EFFICIENCY

Appendix C: ORDINAL MEASURES OF EXPLAINED VARIATION

Appendix C: ORDINAL MEASURES OF EXPLAINED VARIATION

References

References

Index

Index

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Logistic Regression

From Introductory to Advanced Concepts and Applications

Scott Menard
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April 2009 | 392 pages | Sage US

Format Published Date ISBN Price

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.




Table Of Contents:

  • 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

Recent Product Reviews:

Excellent logistic regression book, it outline the use and link it to most of the softwares out there
Professor DANIEL ACHEAMPONG, Accounting Dept, Strayer University - Online
I compared this book to Scott Long's book. I think Long's book is easier to use given that it has a Stata companion. However, I think both texts are very advanced and it would be great to have a more introductory text for graduate students with more limited math skills.
Professor Lorena Barberia, Ciência Política , Universidade de São Paulo
An excellent text. The content was too advanced for an introductory methods course. I would definitely adopt for a more advanced (upper-undergraduate and graduate) course.
Courtney Feldscher, Sociology Dept, University of Massachusetts
To advanced for course.
Professor David Turi, Business Admin Dept, Felician College
Sound book, good level for intermediate level students.
Dr Christos Makrigeorgis, School Of Management, Walden University

Recommendations