Jacob Cohen, Patricia Cohen, Stephen G. West, Leona S. Aiken

Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences (eBook, ePUB)

• Format: ePub

Produktbeschreibung

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.

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Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 536
• Erscheinungstermin: 17. Juni 2013
• Englisch
• ISBN-13: 9781134801015
• Artikelnr.: 42683532

Autorenporträt

Jacob Cohen (Author) , Patricia Cohen (Author) , Stephen G. West (Author) , Leona S. Aiken (Author)

Inhaltsangabe

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.