You are here

PLEASE NOTE: Sage UK Distribution including UK Books Customer Services will be closed for a stocktake from 27th November to 29th November. This affects only book orders and queries from the UK. Any orders placed during this period; or queries emailed, will be dealt with as normal when service resumes on 2nd December. Thank you for your patience and we apologise for any inconvenience caused.

Disable VAT on Taiwan

Unfortunately, as of 1 January 2020 SAGE Ltd is no longer able to support sales of electronically supplied services to Taiwan customers that are not Taiwan VAT registered. We apologise for any inconvenience. For more information or to place a print-only order, please contact uk.customerservices@sagepub.co.uk.

Logistic Regression
Share
Share

Logistic Regression
A Primer

Second Edition


October 2020 | 152 pages | SAGE Publications, Inc

This volume helps readers understand the intuitive logic behind logistic regression through nontechnical language and simple examples. The Second Edition presents results from several statistical packages to help interpret the meaning of logistic regression coefficients, presents more detail on variations in logistic regression for multicategory outcomes, and describes some potential problems in interpreting logistic regression coefficients. A companion website includes the three data sets and Stata, SPSS, and R commands needed to reproduce all the tables and figures in the book. Finally, the Appendix reviews the meaning of logarithms, and helps readers understand the use of logarithms in logistic regression as well as in other types of models.

 
Series Editor’s Introduction
 
Preface
 
Acknowledgments
 
About the Author
 
Chapter 1: The Logic of Logistic Regression
Regression With a Binary Dependent Variable

 
Transforming Probabilities Into Logits

 
Linearizing the Nonlinear

 
Summary

 
 
Chapter 2: Interpreting Logistic Regression Coefficients
Logged Odds

 
Odds

 
Probabilities

 
Standardized Coefficients

 
Group and Model Comparisons of Logistic Regression Coefficients

 
Summary

 
 
Chapter 3: Estimation and Model Fit
Maximum Likelihood Estimation

 
Tests of Significance Using Log Likelihood Values

 
Model Goodness of Fit

 
Summary

 
 
Chapter 4: Probit Analysis
Another Way to Linearize the Nonlinear

 
The Probit Transformation

 
Interpretation

 
Maximum Likelihood Estimation

 
Summary

 
 
Chapter 5: Ordinal and Multinomial Logistic Regression
Ordinal Logistic Regression

 
Multinomial Logistic Regression

 
Summary

 
 
Notes
 
Appendix: Logarithms
The Logic of Logarithms

 
Properties of Logarithms

 
Natural Logarithms

 
Summary

 
 
References
 
Index

Supplements

For instructors

Select a Purchasing Option

SAGE Research Methods is a research methods tool created to help researchers, faculty and students with their research projects. SAGE Research Methods links over 175,000 pages of SAGE’s renowned book, journal and reference content with truly advanced search and discovery tools. Researchers can explore methods concepts to help them design research projects, understand particular methods or identify a new method, conduct their research, and write up their findings. Since SAGE Research Methods focuses on methodology rather than disciplines, it can be used across the social sciences, health sciences, and more.

With SAGE Research Methods, researchers can explore their chosen method across the depth and breadth of content, expanding or refining their search as needed; read online, print, or email full-text content; utilize suggested related methods and links to related authors from SAGE Research Methods' robust library and unique features; and even share their own collections of content through Methods Lists. SAGE Research Methods contains content from over 720 books, dictionaries, encyclopedias, and handbooks, the entire “Little Green Book,” and "Little Blue Book” series, two Major Works collating a selection of journal articles, and specially commissioned videos.