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.
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.
Recenzijas
"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
"Ive 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 books clear, integrated approach to complex statistical modelscoupled with its focus on practical application and ethical considerationshas 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
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
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.
Gary H. McClelland is Professor Emeritus of Psychology at the University of Colorado at Boulder. A faculty fellow at the Institute of Cognitive Science, his research interests include judgment and decision-making, psychological models of economic behavior, statistics and data analysis, and measurement and scaling.
Carey S. Ryan is Professor Emeritus in the Department of Psychology at the University of Nebraska at Omaha. Her research interests include stereotyping and prejudice, group processes, and program evaluation.
