Abstract
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
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