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Best Practices in Logistic Regression

Best Practices in Logistic Regression

© 2015 | 488 pages | SAGE Publications, Inc
Jason W. Osborne’s Best Practices in Logistic Regression provides students with an accessible, applied approach that communicates logistic regression in clear and concise terms. The book effectively leverages readers’ basic intuitive understanding of simple and multiple regression to guide them into a sophisticated mastery of logistic regression. Osborne’s applied approach offers students and instructors a clear perspective, elucidated through practical and engaging tools that encourage student comprehension.

Best Practices in Logistic Regression explains logistic regression in a concise and simple manner that gives students the clarity they need without the extra weight of longer, high-level texts.
1. A Conceptual Introduction to Bivariate Logistic Regression
2. Under the Hood with Logistic Regression
3. Performing Simple Logistic Regression
4. Conceptual and Practical Introduction to Testing Assumptions and Cleaning Data for Logistic Regression
5. Continuous Variables In Logistic Regression (And Why You Should Not Convert Them To Categorical Variables!)
6. Dealing with Unordered Categorical Predictors in Logistic Regression
7. Curvilinear Effects in Logistic Regression
8. Multiple Predictors in Logistic Regression (Including Interaction Effects)
9. A Brief Overview of Probit Regression
10. Logistic Regression and Replication: A Story Of Sample Size, Volatility, and Why Resampling Cannot Save Biased Samples but Data Cleaning And Independent Replication Can
11. Missing Data, Sample Size, Power, and Generalizability of Logistic Regression Analyses
12. Multinomial and Ordinal Logistic Regression: Modeling Dependent Variables with More Than Two Categories
13. Hierarchical Linear Models with Binary Outcomes: Multilevel Logistic Regression


Companion Website
Data sets for the exercises and additional resources are available on the free open-access site.

 “It is a very good text and covers topics, such as the need to clean data, inefficiency/volatility of estimates, and missing data effects, that are not generally dealt with.”

P. Neal Ritchey, University of Cincinnati

“The book includes detailed explanations of various logistic regression models using a range of data and analysis results. It is very suitable for social science students.”

Daoqin Tong, University of Arizona

“This book is concise, accessible, and reader-friendly, particularly for those in education research. The value of this book lies not only in laying out certain “best practices,” but more importantly in pointing out common pitfalls and showing newcomers the way around.”

Yang Cao, University of North Carolina, Charlotte

Recommended for additional reading

Dr Olu A Awosoga
Addictions Counselling, University Of Lethbridge
March 31, 2015

Class was not offered due to lack of interest.

Dr Bal Barot
Science, Lake Michigan Clg-Napier Ave
May 27, 2014
Key features


  • Provides an accessible and applied approach to learning and teaching logistic regression
  • Clear explanations elaborated through useful case examples, vignettes, figures, and tables
  • Numerous computer outputs included to provide visual reference for conceptual material
  • Covers many topics, such as volatility, data cleaning, and nonlinear effects, typically ignored by other texts
  • Explains core topics in a useful, easy-to-read manner
  • The helpful, briefer alternative to dense high-end monographs on the subject

Sample Materials & Chapters

Chapter 1

Chapter 7

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