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Logistic Regression
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Logistic Regression
From Introductory to Advanced Concepts and Applications

  • Scott Menard - Sam Houston State University, USA, University of Colorado, USA


© 2010 | 392 pages | SAGE Publications, Inc

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.

 
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

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
November 28, 2013

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
May 13, 2013

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
July 10, 2012

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
July 10, 2012

To advanced for course.

Professor David Turi
Business Admin Dept, Felician College
May 7, 2012
Key features
  • Thorough coverage of the basic logistic regression model for dichotomous, nominal, and ordinal dependent variables, with no more mathematical notation than is necessary. The material should be accessible even to readers with relatively limited backgrounds in statistics.

 

  • Clear explanation of concepts and illustration with examples. Readers should be able to understand how the concepts are translated into the logistic regression model and then how the results of estimating the model are translated into substantive, English language conclusions. 

 

  • Uniquely detailed coverage of how to assess logistic regression models for dependent variables at different levels of measurement and for data with cases which violate the assumption of independence of observations; including extensive consideration of qualitative (prediction tables) as well as quantitative indices of how well the model predicts the dependent variable. Readers will understand that different criteria apply to the assessment of different types of logistic regression models and will make better decisions about what criteria to apply in evaluating logistic regression models. 

 

  • Coverage of path analysis with logistic regression. This material is unique to this book, and allows the reader to examine not only direct but also indirect effects using logistic regression analysis, much as path analysis is used in multiple linear regression analysis; and coverage includes mixing logistic regression and linear regression in path analysis. 

 

  • Application of logistic regression analysis to longitudinal panel data. Readers will understand how to apply logistic regression to longitudinal data with many cases but relatively few repeated observations in a parallel to linear panel analysis (which is used in conjunction with linear regression analysis and structural equation modeling); and the issues in measuring change with dichotomous, nominal, and ordinal dependent variables. 

 

  • Application of logistic regression to multilevel change analysis. Readers will understand how multilevel change models with logistic regression are different from multilevel growth curve models for continuous interval or ratio scaled dependent variables, how their interpretation is different, and how the multilevel change model can be applied to longitudinal data with many cases and relatively many repeated observations.

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ISBN: 9781412974837

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