Applied multiple regression/correlation analysis for the behavioral sciences

This classic text on multiple regression is noted for its nonmathematical, applied, and data-analytic approach. Readers profit from its verbal-conceptual exposition and frequent use of examples. The applied emphasis provides clear illustrations of the principles and provides worked examples of the types of applications that are possible. Researchers learn how to specify regression models that directly address their research questions. An overview of the fundamental ideas of multiple regression and a review of bivariate correlation and regression and other elementary statistical concepts provide a strong foundation for understanding the rest of the text. The third edition features an increased emphasis on graphics and the use of confidence intervals and effect size measures, and an accompanying website with data for most of the numerical examples along with the computer code for SPSS, SAS, and SYSTAT, at www.psypress.com/9780805822236 . Applied Multiple Regression serves as both a textbook for graduate students and as a reference tool for researchers in psychology, education, health sciences, communications, business, sociology, political science, anthropology, and economics. An introductory knowledge of statistics is required. Self-standing chapters minimize the need for researchers to refer to previous chapters.

Revised edition of: Applied multiple regression/correlation analysis for the behavioral sciences / Jacob Cohen, Patricia Cohen. Second edition. 1983.

Chapter 1. Introduction -- chapter 2. Bivariate correlation and regression -- chapter 3. Multiple regression/correlation with two or more independent variables -- chapter 4. Data visualization, exploration, and assumption checking : diagnosing and solving regression problems I -- chapter 5. Data-analytic strategies using multiple regression/correlation -- chapter 6. Quantitative scales, curvilinear relationships, and transformations -- chapter 7. Interactions among continuous variables -- chapter 8. Categorical or nominal independent variables -- chapter 9. Interactions with categorical variables -- chapter 10. Outliers and multicollinearity : diagnosing and solving regression problems II -- chapter 11. Missing data -- chapter 12. Multiple regression, correlation and causal models -- chapter 13. Alternative regression models : logistic, Poisson regression, and the generalized linear model -- chapter 14. Random coefficient regression and multilevel models -- chapter 15. Longitudinal regression methods -- chapter 16. Multiple dependent variables : set correlation.