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ELEMENTARY REGRESSION MODELING: A Discrete Approach
Roger Wojtkiewicz, Ball State University
Elementary Regression Modeling builds on simple differences between groups to explain regression and regression
modeling. User-friendly and immediately accessible, this book gives readers a thorough understanding of control
modeling, interaction modeling, modeling linearity with spline variables, and creating research hypotheses that serve
as a conceptual basis for many of the processes and procedures quantitative researchers follow when conducting
regression analyses.
CONTENTS 1: Introductory Ideas / 2: Basic Statistical Procedures / 3: Regression Modeling Basics / 4: Key Regression Modeling Concepts / 5:
Control Modeling / 6: Modeling Interactions / 7: Modeling Linearity with Splines / 8: Conclusion: Testing Research Hypotheses
PAPERBACK ISBN: 978-1-5063-0347-5 • MAY 2016 • 240 PAGES • •
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BEST PRACTICES IN LOGISTIC REGRESSION
Jason W. Osborne, Clemson University, University of Louisville
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 regressions to guide them into a sophisticated mastery of logistic regression.
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
PAPERBACK ISBN: 978-1-4522-4479-2 • ©2015 • 488 PAGES • •
online
resources
APPLIED STATISTICS: From Bivariate Through Multivariate Techniques SECOND EDITION
Rebecca M. Warner, University of New Hampshire
This book provides a clear introduction to widely used topics in bivariate and multivariate statistics, including multiple
regression, discriminant analysis, MANOVA, factor analysis, and binary logistic regression. The approach is applied and
does not require formal mathematics; equations are accompanied by verbal explanations. Each chapter presents a
complete empirical research example to illustrate the application of a specifi c method.
CONTENTS 1. Review of Basic Concepts / 2. Basic Statistics, Sampling Error, and Confi dence Intervals / 3. Statistical Signifi cance Testing / 4.
Preliminary Data Screening / 5. Comparing Group Means Using the Independent Samples t Test / 6. One-Way Between-Subjects Analysis of Variance
/ 7. Bivariate Pearson Correlation / 8. Alternative Correlation Coeffi cients / 9. Bivariate Regression / 10. Adding a Third Variable: Preliminary
Exploratory Analyses / 11. Multiple Regression With Two Predictor Variables / 12. Dummy Predictor Variables in Multiple Regression / 13. Factorial
Analysis of Variance / 14. Multiple Regression With More Than Two Predictors / 15. Moderation: Tests for Interaction in Multiple Regression / 16.
Mediation / 17. Analysis of Covariance / 18. Discriminant Analysis / 19. Multivariate Analysis of Variance / 20. Principal Components and Factor
Analysis / 21. Reliability, Validity, and Multiple-Item Scales / 22. Analysis of Repeated Measures / 23. Binary Logistic Regression
HARDCOVER ISBN: 978-1-4129-9134-6 • ©2013 • 1208 PAGES • •
online
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Advanced Statistics – Specific Topics
A PRIMER ON PARTIAL LEAST SQUARES STRUCTURAL EQUATION MODELING
(PLS-SEM) SECOND EDITION
Joseph F. Hair, Jr., Kennesaw State University • G. Tomas M. Hult, Michigan State University • Christian
Ringle, Hamburg University of Technology (TUHH), Germany • Marko Sarstedt, Otto-von-Guericke
University, Magdeburg, Germany
With applications using SmartPLS (www.smartpls.com)—the primary software used in partial least squares structural
equation modeling (PLS-SEM)—this practical guide provides concise instructions on how to use this evolving statistical
technique to conduct research and obtain solutions. Featuring the latest research, new examples, and expanded
discussions throughout, the Second Edition is designed to be easily understood by those with limited statistical and
mathematical training who want to pursue research opportunities in new ways.
CONTENTS 1: An Introduction to Structural Equation Modeling / 2: Specifying the Path Model and Examining Data / 3: Path Model Estimation
/ 4: Assessing PLS-SEM Results Part I: Evaluation of Refl ective Measurement Models / 5: Assessing PLS-SEM Results Part II: Evaluation of the
Formative Measurement Models / 6: Assessing PLS-SEM Results Part III: Evaluation of the Structural Model / 7: Mediator and Moderator Analysis /
8: Outlook on Advanced Methods
PAPERBACK ISBN: 978-1-4833-7744-5 • MARCH 2016 • 384 PAGES • •
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