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 Regression2. Under the Hood with Logistic Regression3. Performing Simple Logistic Regression4. Conceptual and Practical Introduction to Testing Assumptions and Cleaning Data for Logistic Regression5. Continuous Variables In Logistic Regression (And Why You Should Not Convert Them To Categorical Variables!)6. Dealing with Unordered Categorical Predictors in Logistic Regression7. Curvilinear Effects in Logistic Regression8. Multiple Predictors in Logistic Regression (Including Interaction Effects)9. A Brief Overview of Probit Regression10. Logistic Regression and Replication: A Story Of Sample Size, Volatility, and Why Resampling Cannot Save Biased Samples but Data Cleaning And Independent Replication Can11. Missing Data, Sample Size, Power, and Generalizability of Logistic Regression Analyses12. Multinomial and Ordinal Logistic Regression: Modeling Dependent Variables with More Than Two Categories13. 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"