Best Practices in Logistic Regression

Jason W. Osborne is a thought leader and professor in higher education. His background in educational psychology, statistics and quantitative methods, along with that gleaned from high-level positions within Academia gives a unique perspective on the real-world data factors. In 2015, he was appointed Associate Provost and Dean of the Graduate School at Clemson University in Clemson, South Carolina. As well as Associate Provost, at Clemson University, Jason was a Professor of applied statistics at the School of Mathematical Sciences, with a secondary appointment in Public Health Science. In 2019, he took on the role of Provost and Executive VP for Academic Affairs at Miami University. As Provost, Jason implemented a transformative strategic plan to reposition the institution as one prepared for new challenges with a modern, compelling curriculum, a welcoming environment, and enhanced support for student faculty positions and staff. In 2021, he was named by Stanford University as one of the top 2% researchers in the world, underlining his commitment to world-class research methods across particular domains, ultimately influencing a generation of learners. Currently, Jason teaches and publishes on data analysis "best practices" in quantitative and applied research methods. He has served as evaluator or consultant on research projects and in public education (K-12), instructional technology, health care, medicine and business. He served as founding editor of Frontiers in Quantitative Psychology and Measurement and has been on the editorial boards of several other journals (such as Practical Assessment, Research, and Evaluation). Jason W Osborne also publishes on identification with academics and on issues related to social justice and diversity. He has written seven books covering topics to communicate logistic regression and linear modeling, exploratory factor analysis, best practices and modern research methods, data cleaning, and numerous other topics.

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

"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