,“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
,
, Data Analysis
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.
Joshua Correll is a professor of psychology and neuroscience in the College of Arts and Sciences
at the University of Colorado at Boulder. His research examines face processing, stereotypes, and
data analysis.
Abigail (Abby) M. Folberg is an assistant professor of psychology in the College of Arts and
Sciences at the University of Nebraska at Omaha. Her research examines the impacts of stereotypes
and prejudice on marginalized group members as well as how individuals and organizations can
reduce prejudice and discrimination.
Charles “Chick” M. Judd is Professor Emeritus of Distinction in the College of Arts and Sciences
at the University of Colorado at Boulder. His research focuses on social cognition and attitudes,
intergroup relations and stereotypes, judgment and decision-making, and behavioral science
research methods and data analysis.
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
,
, Data Analysis
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.
Joshua Correll is a professor of psychology and neuroscience in the College of Arts and Sciences
at the University of Colorado at Boulder. His research examines face processing, stereotypes, and
data analysis.
Abigail (Abby) M. Folberg is an assistant professor of psychology in the College of Arts and
Sciences at the University of Nebraska at Omaha. Her research examines the impacts of stereotypes
and prejudice on marginalized group members as well as how individuals and organizations can
reduce prejudice and discrimination.
Charles “Chick” M. Judd is Professor Emeritus of Distinction in the College of Arts and Sciences
at the University of Colorado at Boulder. His research focuses on social cognition and attitudes,
intergroup relations and stereotypes, judgment and decision-making, and behavioral science
research methods and data analysis.
