Designing Experiments and Analyzing Data: A Model Comparison Perspective
New edition! In late 2026, the 4th edition was published, offering improvements throughout. There is a deeply integrative conceptual framework for understanding experimental design and data analysis, as well as computational support in the text and on this site, so as easily go from concept to analysis.
This site accompanies Designing Experiments and Analyzing Data: A Model Comparison Perspective (4th edition; Maxwell, Delaney, & Kelley, 2027). This book won the Roger E. Kirk Authors of the Year Award from Division 5 of the American Psychological Association, and the prior edition won the Barbara Byrne Award for Outstanding Book or Edited Volume from the Society of Multivariate Experimental Psychology. The book builds on the model comparison perspective espoused since the publication of the first edition in 1990.
This site provides supplementary material to facilitate implementing the methods discussed in the book with computing examples (in R, SPSS, & SAS) and data files for all of the datasets used in the book. We hope that site is a valuable resource for readers and instructors. Please contact us if you have suggestions for improvement.
Table of Contents
Data
Chapter Data
- AMCP – An R Package of Data Sets
- CSV Data Files (Comma Space Delimited)
- SPSS Data Files
- SAS Data Files
Exercise Data
- AMCP – An R Package of Data Sets
- CSV Data Files (Comma Space Delimited)
- SPSS Data Files
- SAS Data Files
Shiny R Web Apps
Shiny web apps are online applications that use R on a server (instead of a local machine).
These apps are based on the full power of the R language and environment for statistical computing, but do not require the user to install R or even know anything about R.
The apps are generally very simple to use.
Supplements
Solutions
Solutions Manual for Selected Exercises (i.e., the Starred Exercises)
- CONCEPTUAL BASES OF EXPERIMENTAL DESIGN AND ANALYSIS
- MODEL COMPARISONS FOR BETWEEN SUBJECTS DESIGNS
- MODEL COMPARISONS FOR DESIGNS INVOLVING WITHIN—SUBJECTS FACTORS
- MIXED-EFFECTS MODELS
About the Book
Designing Experiments and Analyzing Data: A Model Comparison Perspective (3rd edition) offers an integrative conceptual framework for understanding experimental design and data analysis.
The authors (Scott E. Maxwell, Harold D. Delaney, and Ken Kelley) first apply fundamental principles to simple experimental designs followed by an application of the same principles to more complicated designs. Their integrative conceptual framework better prepares readers to understand the logic behind a general strategy of data analysis that is appropriate for a wide variety of designs, which allows for the introduction of more complex topics that are generally omitted from other books.
About the Authors
Scott E. Maxwell
Scott E. Maxwell is the Fitzsimons Professor of Psychology at the University of Notre Dame. His research interests are in the areas of research methodology and applied behavioral statistics, with much of his recent work focusing on statistical power and accuracy in parameter estimation, especially in randomized designs.
He has served as editor of Psychological Methods; received the Samuel J. Messick Award for Distinguished Scientific Contributions by the American Psychological Association’s Division of Evaluation, Measurement, and Statistics; and has received multiple teaching awards.
Harold D. Delaney
Harold D. Delaney is Emeritus Professor of Psychology at the University of New Mexico, where he received the University’s Outstanding Graduate Teacher of the Year award for his course on experimental design and analysis, and where he directed the Psychology Honors program for 30 years.
His research interests in applied statistics include methods that accommodate individual differences among people. He received a Fulbright Award from the U.S. Department of State to spend an academic year lecturing in Budapest, Hungary, and continues to offer courses there.
Ken Kelley
Ken Kelley is the Edward F. Sorin Society Professor of Information Technology, Analytics, and Operations in the Mendoza College of Business at the University of Notre Dame. He is co-director of the Human-centered Analytics Lab (HAL), which is an interdisciplinary lab focused on the interaction of persons and technology, largely based on generative AI.
His work advances how to design studies, measure constructs, and analyze data, particularly in organizational contexts and digital environments. His goal is to make human-centered science better by bridging rigorous methods with important applications to help ask better questions and make more scientifically valid conclusions.
He is an Accredited Professional Statistician (PStat®); recipient of the Anne Anastasi early career award by the Evaluation, Measurement, & Statistics Division of the APA, and previously served as an associate editor of Psychological Methods. He is a fellow of the Society of Psychological Science and the American Psychological Association, and an elected member of the Society of Multivariate Experimental Psychology.
References:
Maxwell, S. E., Delaney, H. D., & Kelley, K. (2027). Designing experiments and analyzing data: A model comparison perspective (4th ed). New York, NY: Routledge.