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More About This Textbook
Overview
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.
Editorial Reviews
From the Publisher
"...anyone with an interest in or need to carry out MR will find it invaluable." - British Journal of Mathematical and Statistical Psychology"I can't see how a practicing research psychologist could be without a copy." - Dr. Steven P. Reise, University of California at Los Angeles
"Overall we think the book successfully reflects more contemporary issues, thinking, and advances in multiple regression and correlation....The general style and format, consistent with the previous editions, is understandable while still retaining depth and comprehensiveness." - Drs. Lynda A. and Daniel W. King, Boston University School of Medicine
"I think VERY highly of this revision....The chapter on regression diagnostics and graphics (Ch. 4) is a wonderful addition. Also, the emphasis on confidence intervals is great, as is the extensive discussion on logistic regression." - Dr. William F. Chaplin, University of Alabama, Tuscaloosa
"I have always felt that the book's major strength was its ability to blend careful, thoughtful explanation with a relatively non-technical exposition....I am happy to say that in my opinion this edition retains and furthermore expands upon this strength." - Dr. Harry Reis, University of Rochester
Booknews
Since the publication of the first edition, multiple regression is increasingly used in the social and behavioral sciences. In this new edition, the material is not substantially changed aside from a down-playing of the use of analysis of variance, increased emphasis on graphical representation, and a moving away from reliance on correlational statistics. Chapters cover bivariate and multiple correlation and regression; data visualization; data analytic strategies; quantitative scales, curvilinear relationships, and transformations; continuous, nominal independent, and categorical variables; outliers and multicollinearity; missing data; causal models; alternative regression models; set correlation with multiple dependent values. The text does not make clear what the CD-ROM contains. Annotation c. Book News, Inc., Portland, OR (booknews.com)Product Details
Related Subjects
Table of Contents
Contents: Preface. Introduction. Bivariate Correlation and Regression. Multiple Regression/Correlation With Two or More Independent Variables. Data Visualization, Exploration, and Assumption Checking: Diagnosing and Solving Regression Problems I. Data-Analytic Strategies Using Multiple Regression/Correlation. Quantitative Scales, Curvilinear Relationships, and Transformations. Interactions Among Continuous Variables. Categorical or Nominal Independent Variables. Interactions With Categorical Variables. Outliers and Multicollinearity: Diagnosing and Solving Regression Problems II. Missing Data. Multiple Regression/Correlation and Causal Models. Alternative Regression Models: Logistic, Poisson Regression, and the Generalized Linear Model. Random Coefficient Regression and Multilevel Models. Longitudinal Regression Methods. Multiple Dependent Variables: Set Correlation. Appendices: The Mathematical Basis for Multiple Regression/Correlation and Identification of the Inverse Matrix Elements. Determination of the Inverse Matrix and Applications Thereof.