9781138819825-1138819824-Data Analysis: A Model Comparison Approach To Regression, ANOVA, and Beyond, Third Edition

Data Analysis: A Model Comparison Approach To Regression, ANOVA, and Beyond, Third Edition

ISBN-13: 9781138819825
ISBN-10: 1138819824
Edition: 3
Author: Judd, Charles M., McClelland, Gary H., Ryan, Carey S.
Publication date: 2017
Publisher: Routledge
Format: Hardcover 378 pages
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Book details

ISBN-13: 9781138819825
ISBN-10: 1138819824
Edition: 3
Author: Judd, Charles M., McClelland, Gary H., Ryan, Carey S.
Publication date: 2017
Publisher: Routledge
Format: Hardcover 378 pages

Summary

Acknowledged authors Judd, Charles M., McClelland, Gary H., Ryan, Carey S. wrote Data Analysis: A Model Comparison Approach To Regression, ANOVA, and Beyond, Third Edition comprising 378 pages back in 2017. Textbook and eTextbook are published under ISBN 1138819824 and 9781138819825. Since then Data Analysis: A Model Comparison Approach To Regression, ANOVA, and Beyond, Third Edition textbook was available to sell back to BooksRun online for the top buyback price or rent at the marketplace.

Description

Data Analysis: A Model Comparison Approach to Regression, ANOVA, and Beyond is an integrated treatment of data analysis for the social and behavioral sciences. It covers all of the statistical models normally used in such analyses, such as multiple regression and analysis of variance, but it does so in an integrated manner that relies on the comparison of models of data estimated under the rubric of the general linear model.

Data Analysis also describes how the model comparison approach and uniform framework can be applied to models that include product predictors (i.e., interactions and nonlinear effects) and to observations that are nonindependent. Indeed, the analysis of nonindependent observations is treated in some detail, including models of nonindependent data with continuously varying predictors as well as standard repeated measures analysis of variance. This approach also provides an integrated introduction to multilevel or hierarchical linear models and logistic regression. Finally, Data Analysis provides guidance for the treatment of outliers and other problematic aspects of data analysis. It is intended for advanced undergraduate and graduate level courses in data analysis and offers an integrated approach that is very accessible and easy to teach.

Highlights of the third edition include:

  • a new chapter on logistic regression;
  • expanded treatment of mixed models for data with multiple random factors;
  • updated examples;
  • an enhanced website with PowerPoint presentations and other tools that demonstrate the concepts in the book; exercises for each chapter that highlight research findings from the literature; data sets, R code, and SAS output for all analyses; additional examples and problem sets; and test questions.

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