Data Analysis by Correll, Josh; Folberg, Abigail M.; Judd, Charles M.;

Short description:

This essential textbook provides an integrated treatment of data analysis for the social and behavioral sciences. It covers all the key statistical models in an integrated manner that relies on the comparison of models of data estimated under the rubric of the general linear model.

Long description:

This essential textbook provides an integrated treatment of data analysis for the social and behavioral sciences. It covers all the key statistical models in an integrated manner that relies on the comparison of models of data estimated under the rubric of the general linear model.

The text describes the foundational logic of the unified model comparison framework. It then shows how this framework can be applied to increasingly complex models including multiple continuous and categorical predictors, as well as product predictors (i.e., interactions and nonlinear effects). The text also describes analyses of data that violate assumptions of independence, homogeneity, and normality. The analysis of nonindependent data is treated in some detail, covering standard repeated measures analysis of variance and providing an integrated introduction to multilevel or hierarchical linear models and logistic regression.

Highlights of the fourth edition include:

• Expanded coverage of generalized linear models and logistic regression in particular


• A discussion of power and ethical statistical practice as it relates to the replication crisis


• An expanded collection of online resources such as PowerPoint slides and test bank for instructors, additional exercises and problem sets with answers, new data sets, practice questions, and R code

Clear and accessible, this text is intended for advanced undergraduate and graduate level courses in data analysis.

Access the Instructor Resources for this title at routledgetextbooks.com/textbooks/instructor_downloads

"Most introductory statistics texts teach students how to apply specific tests in specific circumstances, with little room for generalizing knowledge to new settings. Data Analysis instead teaches students how to think like scientists, always framing hypotheses as formal comparisons between competing explanations. The first three editions were ahead of their time in their philosophical approach to data analysis, and this new edition retains and expands their unifying framework."

Kristopher J. Preacher, Vanderbilt University, USA

"I am delighted that both logistic regression and multilevel modeling are now included. Both topics are introduced using the authors’ clear, useful, and integrative approach. Not only does the new material help me to teach this to my students better, it also helps me to understand the topics better!"

J. Michael Bailey, Northwestern University, USA

"I’ve relied on previous editions of Data Analysis: A Model Comparison Approach to Regression, ANOVA, and Beyond for years in my graduate-level data analysis courses. The book’s clear, integrated approach to complex statistical models—coupled with its focus on practical application and ethical considerations—has made it an indispensable resource for both students and instructors. This latest edition continues to be a top choice for mastering advanced data analysis techniques."

Markus Brauer, University of Wisconsin-Madison, USA

Table of Contents:

Section A: Statistical Machinery  1. Introduction to Data Analysis 2. Simple Models: Definitions of Error and Parameter Estimates 3. Simple Models: Models of Error and Sampling Distributions 4. Simple Models: Statistical Inferences about Parameter Estimates 5. Statistical Power: Power, Effect Sizes, and Confidence Intervals  Section B: Increasingly Complex Models  6. Simple Regression: Models with a Single Continuous Predictor 7. Multiple Regression: Models with Multiple Continuous Predictors 8. Moderated and Nonlinear Multiple Regression models 9. One-Way ANOVA: Models with a Single Categorical Predictor 10. Factorial ANOVA: Models with Multiple Categorical Predictors and Product Terms 11. ANCOVA: Models with Continuous and Categorical Predictors  Section C: Violations of Assumptions About Error  12. Repeated-Measures ANOVA: Models with Nonindependent Errors 13. Incorporating Continuous Predictors with Nonindependent Data: Towards Mixed Models 14. Outliers and Ill-Mannered Error 15. Logistic Regression: Dependent Categorical Variables