Best Practices in Logistic Regression

Jason W. Osborne - Clemson University, USA

March 2014 | 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.

Available Formats

ISBN: 9781452244792 Paperback Suggested Retail Price: $95.00 Bookstore Price: $76.00

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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

Supplements

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

“This text is extremely student-friendly . . . it is a nearly perfect balance of conceptual explanation and application using example data sets”

Denna L. Wheeler, Oklahoma State University Center for Health Sciences

“This is an absolutely stellar approach to a very difficult and under-used analysis. The use of humor, practical examples, the use of real data, and the inclusion of both basic and advanced concepts without being overly concerned with the derivation of the analysis, foster a better understanding of logistic regression.”

Frank B. Underwood, University of Evansville

“The text will serve well to widely expand the usage of the logistic regression in social science research. The not-too-technical explanation of core concepts, with numerous computer outputs for illustrations, makes it a perfect text for the senior undergraduate and graduate-level course, as well as a reference for the analytical practitioner.”

Professor David Han, University of Texas, San Antonio

“I appreciate the emphasis on application and the coverage of topics that are useful in research but neglected by other books on this method.”

Dr. Chuck W. Peek, University of Florida

“This is a very impressive book. The topic is timely.”

Shanta Pandey, Washington University, St. Louis

“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