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

Linear Regression Analysis for Social and Behavioral Sciences

Have you ever struggled with understanding linear regression analysis? This user-friendly text by Richard B. Darlington and Andrew F. Hayes is here to help. With an emphasis on conceptual understanding over mathematical complexity, this comprehensive guide introduces students and researchers to linear regression analysis in the social, behavioral, consumer, and health sciences.

Comprehensive Coverage of Key Topics

  • Model construction and estimation
  • Quantification and measurement of multivariate and partial associations
  • Statistical control and group comparisons
  • Moderation analysis, mediation, and path analysis
  • Regression diagnostics and more

Practical Examples and Helpful Advice

Engaging worked-through examples demonstrate each technique, accompanied by helpful advice and cautions to ensure you master each concept. The authors emphasize the use of popular statistical software, including SPSS, SAS, and STATA, with an appendix on regression analysis using R.

Practice What You Learn

The companion website (www.afhayes.com) provides datasets for the book’s examples, as well as the RLM macro for SPSS and SAS. This allows you to practice what you learn in each chapter and reinforce your understanding of linear regression analysis.

Pedagogical Features

  • Chapters include SPSS, SAS, or STATA code pertinent to the analyses described, with each distinctly 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.

Unique Coverage of Important Topics

This text 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.

Book Details

  • Publisher: The Guilford Press
  • Illustrated edition: September 27, 2016
  • Language: English
  • Hardcover: 661 pages
  • ISBN-10: 1462521134
  • ISBN-13: 978-1462521135

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