Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences

Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences

Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences

Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences

Synopsis

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 CD with data for most of the numerical examples along with the computer code for SPSS, SAS, and SYSTAT. 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.

Excerpt

The seven years since the publication of the first edition have been fat ones for multiple regression/correlation as a general data-analytic system ("new-look" MRC, in short). The behavioral and social science journals have carried hundreds of pages on methodological issues in MRC, (much of it on how--when--whether it can replace other methods), and, increasingly, research reports that employ it. Several MRC textbooks and chapter-length treatments have appeared, and MRC interest groups have formed as part of specialized scientific organizations. "New-look" MRC has been applied across a spectrum that reaches from the molecular end of psychology through the molar end of sociology, and has been particularly popular in education, the evaluation of intervention programs, drug research, and psychiatric epidemiology. Its obvious relevance to "meta-analysis" has not been overlooked.

While much of the "nuts and bolts" in the original edition remains intact, there has been a fundamental change in outlook. The relevance of MRC to the study of causality, dimly hinted at here and there in the first edition, now emerges as the central principle. From the very beginning in the presentation of the two-variable regression equation, through the interpretation of patterns of association with two independent variables, alternative analytic strategies, and setwise hierarchical analysis, the intimate relationship of MRC to the formal analysis of causal models is described and illustrated. After the methods of representing information as data for MRC are presented, a chapter is devoted to the analysis of causal models, and simple working methods employing systems of regression equations are provided.

The detailed exposition of the analysis of covariance is preceded by a causal models analysis of the types of research design that employ this method. Throughout, the exposition reflects our conviction that the valid analysis of nonexperimental data can proceed only when it is in keeping with the principles and insights of the analysis of causal models.

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