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

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This book introduces linear regression analysis in a user-friendly way, emphasizing conceptual understanding over mathematics. It covers topics such as model construction and estimation, quantification and measurement of associations, statistical control, group comparisons, moderation analysis, mediation and path analysis, and regression diagnostics. The book includes examples and code in SPSS, SAS, and STATA, with an appendix on regression analysis using R. The eBook is 661 pages and was published in September 2016 by The Guilford Press. ISBN-10: 1462521134, ISBN-13: 978-1462521135.

Category: Medical Book

Description

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

By Richard B. Darlington, Andrew F. Hayes

This user-friendly book provides a comprehensive description of linear regression analysis, it is intended for students and researchers in the social, behavioral, consumer, and health fields. The book focuses on the conceptual understanding of topics rather than the mathematical complexity of the subject, the book covers a wide variety of topics that are essential to conducting regression analysis in these fields.

The book’s first chapter introduces the fundamental concepts of model creation and estimation, it then guides readers through the process of constructing regression models. It then discusses the various significant aspects of regression analysis, including the measurement and quantification of multiple variables, the statistical control of the data, the comparison of groups, the analysis of mediation, and the diagnosis of regression.

Employing practical examples that are worked through accompany each method, these examples demonstrate their practical application. The examples are accompanied by helpful suggestions and cautions that will ensure a comprehensive understanding of the concepts. The book advocates the use of popular statistical software like SPSS, SAS, and STATA, it also contains an appendix on how to use R for regression analysis.

pedagogical features enhance the learning experience:

Chapters contain code samples in SPSS, SAS, or STATA that are relevant to the analyses described, with a clear format that facilitates the identification of the code.

An appendix contains documentation for the RLM macro, this documentation facilitates the computation of various metrics like estimating and probing interactions, dominance analysis, heteroscedasticity consistent standard errors, and linear spline regression.

Students are permitted to practice the methods of each chapter utilizing datasets accessible online.

The book extends past the traditional regression model and addresses topics that are not commonly covered, such as measuring the importance of variables, coding systems for categorical variables, causation, and the falsification of myths about the testing of interactions.

To promote the learning process, the companion my website offers datasets for the examples in the book as well as the RLM macro for SPSS and SAS.

This comprehensive book gives students and researchers a solid understanding of regression analysis, which enables them to utilize this technique in their respective fields.