Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences by Jacob Cohen, Patricia Cohen, Stephen G. West, Leona S. Aiken

Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences by Jacob Cohen, Patricia Cohen, Stephen G. West, Leona S. Aiken

          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.

Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences by Jacob Cohen, Patricia Cohen, Stephen G. West, Leona S. Aiken 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

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
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Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences by Jacob Cohen, Patricia Cohen, Stephen G. West, Leona S. Aiken Review

This is one of the best applied statistics books I've come across. It's particularly good in its coverage of continuous by continuous variable interactions in regression as well as considering basic issues of hypothesis testing in multivariate regression. I also appreciate its discussion of historical traditions which have lead some fields to be more dependent upon regression and some more ANOVA. I haven't seen one other text that comes even close.

It can be a little difficult to connect with contemporary statistics software (e.g. Stata) and terminology at times. For example, they refer to a technique called Fisher's Protected Least Square Difference Test, which I believe to be the same a joint significance in more common regression parlance, but it is hard to confirm. These type of problems are to be expected with different fields using different terms.

One negative reviewer complains that this book is difficult. This depends on what kind of text one is comparing it to. For the deep level of very useful information that it covers, it is the most readable text I've encountered.

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