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Regression Analysis and Linear Models: Concepts, Applications, and
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Regression Analysis and Linear Models: Concepts, Applications, and Implementation Hardback - 2016 - 1st Edition

by Richard B. Darlington; Andrew F. Hayes

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Reader reviews for Regression Analysis and Linear Models: Concepts, Applications, and Implementation

From the publisher

Emphasizing conceptual understanding over mathematics, this user-friendly text introduces linear regression analysis to students and researchers across the social, behavioral, consumer, and health sciences. Coverage includes model construction and estimation, quantification and measurement of multivariate and partial associations, statistical control, group comparisons, moderation analysis, mediation and path analysis, and regression diagnostics, among other important topics. Engaging worked-through examples demonstrate each technique, accompanied by helpful advice and cautions. The use of SPSS, SAS, and STATA is emphasized, with an appendix on regression analysis using R. The companion website (www.afhayes.com) provides datasets for the book's examples as well as the RLM macro for SPSS and SAS.

Pedagogical Features:
*Chapters include SPSS, SAS, or STATA code pertinent to the analyses described, with each distinctively formatted for easy identification.
*An appendix documents the RLM macro, which facilitates computations for estimating and probing interactions, dominance analysis, heteroscedasticity-consistent standard errors, and linear spline regression, among other analyses.
*Students are guided to practice what they learn in each chapter using datasets provided online.
*Addresses topics not usually covered, such as ways to measure a variable's importance, coding systems for representing categorical variables, causation, and myths about testing interaction.

Details

  • Title Regression Analysis and Linear Models: Concepts, Applications, and Implementation
  • Author Richard B. Darlington; Andrew F. Hayes
  • Binding Hardback
  • Edition number 1st
  • Edition 1
  • Pages 661
  • Volumes 1
  • Language ENG
  • Publisher Guilford Publications
  • Publication date 2016-09
  • Illustrated Yes
  • Features Bibliography, Illustrated, Index
  • ISBN 9781462521135 / 1462521134
  • Weight 2.95 lbs (1.34 kg)
  • Dimensions 10 x 6.8 x 1.4 in (25.40 x 17.27 x 3.56 cm)
  • Category Sociology
  • Library of Congress subjects Social sciences - Statistical methods, Psychology - Statistical methods
  • Library of Congress Catalogue Number 2016034313
  • Dewey Decimal Code 001.422

About the author

Richard B. Darlington, PhD, is Emeritus Professor of Psychology at Cornell University. He is a Fellow of the American Association for the Advancement of Science and has published extensively on regression and related methods, the cultural bias of mental tests, the long-term effects of preschool programs, and, most recently, the neuroscience of brain development and evolution.

Andrew F. Hayes, PhD, is Distinguished Research Professor in the Haskayne School of Business at the University of Calgary, Alberta, Canada. His research and writing on data analysis has been published widely. Dr. Hayes is the author of Introduction to Mediation, Moderation, and Conditional Process Analysis and Statistical Methods for Communication Science, as well as coauthor, with Richard B. Darlington, of Regression Analysis and Linear Models. He teaches data analysis, primarily at the graduate level, and frequently conducts workshops on statistical analysis throughout the world. His website is www.afhayes.com.

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Regression Analysis and Linear Models: Concepts, Applications, and Implementation (Methodology in the Social Sciences Series)

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Regression Analysis and Linear Models: Concepts, Applications, and Implementation (Methodology in the Social Sciences Series)

by Darlington, Richard B.; Hayes, Andrew F.

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Regression Analysis and Linear Models: Concepts, Applications, and Implementation (Methodology in the Social Sciences)

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Regression Analysis and Linear Models: Concepts, Applications, and Implementation (Methodology in the Social Sciences)

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