Designing Experiments and Analyzing Data
Description
Designing Experiments and Analyzing Data: A Model Comparison Perspective (Fourth edition)
offers an updated version of the award-winning text that has influenced the social sciences for
more than a generation.Maxwell, Delaney, and 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. Numerous pedagogical features further facilitate understanding: examples of published research demonstrate the applicability of each chapter's content; flowcharts assist in choosing the most appropriate procedure; end-of-chapter lists of important formulas highlight key ideas and assist readers in locating the initial presentation of equations; useful programming code and tips are provided throughout the book and in associated resources available at DesigningExperiments.Com; and extensive exercises help develop a deeper understanding of the topics. Detailed solutions for some of the exercises and realistic data sets are included on their website, DesigningExperiments.Com. The pedagogical approach used throughout the book enables readers to gain an overview of experimental design, from conceptualization of the research question to analysis of the data. The book and its companion website with web apps, tutorials, and detailed code are ideal for students and researchers seeking the optimal way to design their studies and analyze the resulting data.
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Persons
Scott E. Maxwell is the Fitzsimons Professor of Psychology Emeritus 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; Saul B. Sells Award for Distinguished Multivariate Research from the Society of Multivariate Behavioral Research; and has received multiple teaching awards, including the Jacob Cohen Distinguished Contributions to Teaching and Mentoring Award from the American Psychological Association. Maxwell is a fellow of the American Psychological Association, a fellow of the Association for Psychological Science, and an elected member of the Society of Multivariate Experimental Psychology.
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. 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 at Eötvös Loránd University in Budapest, Hungary, and in recent years he has offered courses through the Institute of Psychology of Károli Gáspár University in Budapest.
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 with a concurrent
(courtesy) appointment in the Department of Psychology. Kelley is co-founder and co-director of the Human-centered Analytics Lab (HAL), an interdisciplinary research lab that examines the interaction of people and technology to better understand the human condition in our increasingly digital world. His work focuses on the development, improvement, and evaluation of statistical methods and measurement in the context of human-centered research. He is an Accredited Professional Statistician (PStat®); recipient of the Anne Anastasi early career award by the APA's Division of Evaluation, Measurement, and Statistics; a fellow of the American Psychological Association; a fellow of the Association for Psychological Science, and an elected member of the Society of Multivariate Experimental Psychology.
Content
Preface Part I Conceptual Bases of Experimental Design and Analysis 1. The Logic of Experimental Design and Analysis 2. Drawing Valid Inferences From Experiments Part II Model Comparisons for Between-Subjects Designs 3. Introduction to Model Comparisons: One-Way Between-Subjects Designs 4. Individual Comparisons of Means 5. Testing Several Contrasts: The Multiple-Comparisons Problem 6. Trend Analysis 7. Two-Way Between-Subjects Factorial Designs 8. Higher-Order Between-Subjects Factorial Designs 9. DesignsWith Covariates: ANOVA and Blocking 10. Designs with Random or Nested Factors Part III Model Comparisons for Designs Involving Within-Subjects Factors 11. One-WayWithin-Subjects Designs: Univariate Approach 12. Higher-Order Designs with Within-Subjects Factors: Univariate Approach 13. One-WayWithin-Subjects Designs: Multivariate Approach 14. Higher Order Designs WithWithin-Subjects Factors: The Multivariate Approach Part IV Mixed-Effects Models 15. An Introduction to Mixed-Effects Models: Within-Subjects Designs 16. An Introduction to Mixed-Effect Models: Nested Designs Statistical Tables