This classic text on multiple regression is noted for its non-mathematical, applied, and data-analytic approach intended to teach the reader "how to do it." Students and researchers profit from its verbal-conceptual exposition and frequent use of concrete 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 of interest. Early in the text 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 a solid understanding of the rest of the text. The third edition reflects both the current and developing state-of-the-art practices in the field: *An increased emphasis on graphics provides greater understanding of data. *An increased emphasis on the use of confidence intervals and effect size measures provides more information about the size and precision of relationships. *An accompanying CD contains data for most of the numerical examples along with the computer code for SPSS, SAS, and SYSTAT. These computer scripts can serve as templates for the analysis of the student's own data. *Five entirely new chapters are included: Assumptions of the regression model and remedies when they are not met (Ch. 4), detection and treatment of the potential problems of outliers and multicollinearity (Ch. 10), alternative regression models that may be used when the dependent variable is binary, ordered category, or count in form, including logistic, ordinal logistic, Poisson regression, and the generalized linear model (Ch. 13), multilevel models for data collected in groups or other clusters (Ch. 14), and the analysis of longitudinal data (Ch. 15). *Extensively revised chapters on curvilinear relationships and transformations (Ch. 6), interactions between continuous variables (Ch. 7), interactions between categorical and continuous variables (Ch. 9), and missing data (Ch. 11) reflect the latest developments in these areas. *Clear coverage of classic issues in regression and correlation with one or more continuous or categorical predictors is provided. *A new end-of-text glossary provides definitions of key terms. *A new appendix featuring statistical symbols, abbreviations, tests, and functions serves as a handy reference tool. Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences serves as both a textbook for graduate students and as a reference tool for researchers in the basic and applied behavioral sciences, including psychology, education, health sciences, communications, business, sociology, political science, anthropology, and economics. Only an introductory knowledge of statistics is required. Largely self-standing chapters minimize the need for researchers to refer to previous chapters. The book is an ideal text for courses on basic and advanced topics in multiple regression and correlational methods.