Regression Analysis And Linear Models Concepts, Applications, And Implementation (Methodology In The Social Sciences) (Original PDF From Publisher)

1.1.Description

Prioritizing conceptual comprehension over mathematical intricacies, this user-friendly text serves as a comprehensive introduction to linear regression analysis for students and researchers in the social, behavioral, consumer, and health sciences.

The coverage encompasses various aspects such as model construction, estimation, quantification, multivariate and partial associations, statistical control, group comparisons, moderation analysis, mediation and path analysis, and regression diagnostics.

Worked-through examples are presented with practical advice and cautions, highlighting the utilization of SPSS, SAS, and STATA, with an additional focus on regression analysis using R. The companion website (www.afhayes.com) offers datasets for examples and the RLM macro for SPSS and SAS.

With distinctive formatting for code, the chapters facilitate easy identification, guiding students to practice concepts using online datasets.

Additionally, “Regression Analysis And Linear Models: Concepts, Applications, And Implementation” addresses unconventional topics, including variable importance measurement, categorical variable coding systems, causation, and dispelling myths about testing interactions.

Regression Analysis And Linear Models Concepts, Applications, And Implementation (Methodology In The Social Sciences) (Original PDF From Publisher)

1.2.Key Features

The book, “Regression Analysis And Linear Models: Concepts, Applications, And Implementation” prioritizes conceptual understanding without delving too deeply into mathematics.

It serves as a user-friendly guide for students and researchers in various sciences. The content covers model construction, estimation, quantification, multivariate and partial associations, statistical control, group comparisons, moderation analysis, mediation and path analysis, and regression diagnostics. Engaging examples are provided with practical advice and cautions.

The emphasis on using SPSS, SAS, and STATA is reinforced, and an appendix introduces regression analysis using R. The companion website offers datasets for hands-on practice.

Unique features include distinctive formatting for code, addressing unconventional topics, and dispelling myths about testing interactions. “Regression Analysis And Linear Models: Concepts, Applications, And Implementation” provides a well-rounded approach to linear regression analysis in a variety of disciplines.

Regression Analysis And Linear Models Concepts, Applications, And Implementation (Methodology In The Social Sciences) (Original PDF From Publisher)

1.3. About Writer

Richard B. Darlington and Andrew F. Hayes are accomplished figures in the field of statistics and social sciences. Richard B. Darlington, known for his expertise in statistical methodology, has contributed significantly to the development and application of statistical techniques in the social and behavioral sciences.

Andrew F. Hayes, a distinguished researcher and educator, is renowned for his work in quantitative methods and statistical analysis. Both scholars have made substantial contributions to the understanding and advancement of regression analysis.

Their collaborative efforts have led to the creation of valuable resources, including the book “Regression Analysis And Linear Models: Concepts, Applications, And Implementation” which reflects their commitment to providing accessible and comprehensive guidance in the field of statistics and linear modeling.

Their combined achievements have had a lasting impact on researchers and students alike, influencing the way regression analysis is approached and understood across various disciplines.

Regression Analysis And Linear Models Concepts, Applications, And Implementation (Methodology In The Social Sciences) (Original PDF From Publisher)

Summary

“Regression Analysis And Linear Models: Concepts, Applications, And Implementation” serves as a user-friendly guide to linear regression analysis in the social, behavioral, consumer, and health sciences.

Emphasizing conceptual understanding of complex mathematics, the book covers essential topics such as model construction, estimation, multivariate associations, statistical control, group comparisons, moderation analysis, mediation, and regression diagnostics.

The authors, Richard B. Darlington and Andrew F. Hayes present engaging examples with SPSS, SAS, and STATA code, encouraging practical application. The inclusion of an appendix on regression analysis using R adds versatility.

The book addresses topics not commonly covered, offering a valuable resource for students and researchers seeking a comprehensive and accessible approach to regression analysis and linear models.