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Cited by 20,737

The DOI matches its Crossref record.

Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences

Cohen, Patricia, Cohen, Patricia, West, Stephen G., Aiken, Leona S.·2014·Psychology Press eBooks
DOI 10.4324/9781410606266
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Abstract

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.

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Metadata

Authors
Cohen, P., Cohen, P., West, S. G., & Aiken, L. S.
Venue
Psychology Press eBooks
Publisher
Psychology Press
Published
2014-04-04
DOI
10.4324/9781410606266
OpenAlex
W2159401492
Cited by
20,737
References
0
FWCI
281.63
Type
book
Language
en
Open access
closed

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.