Best practices in logistic regression. (2014)

• Record Type: Book
• Further Information: Note: Jason W. Osborne, University of Louisville.
• Authors: Osborne, Jason W
• Contents: 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
• Publisher Details: Thousand Oaks : SAGE Publications, Inc
• Publication Date: 2014
• Extent: 1 online resource (488 pages)
• Subjects: 519.5/36; Logistic regression analysis
• Languages: English
• ISBNs: 9781483312095; 1483312097
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.