Research Design and Statistical Analysis

Caren M. Rotello is Professor Emerita at the University of Massachusetts Amherst. She received her Ph.D. in Psychology from Stanford University.Jerome L. Myers is Professor Emeritus at the University of Massachusetts Amherst. He received his Ph.D. in Psychology from the University of Wisconsin.Arnold D. Well is Professor Emeritus at the University of Massachusetts Amherst. He received his Ph.D. in Experimental Psychology from the University of Oregon.Robert F. Lorch, Jr., is Professor Emeritus at the University of Kentucky. He received his Ph.D. in Psychology from the University of Massachusetts Amherst.

“Having worn out the 3rd edition to the point of disintegration, I was pleased to learn of this new edition. The authors have elevated the book to new heights by Rotello’s lucid and precise expository narrative and the incorporation of R.”Chad Dubé, Associate Professor of Psychology, University of South Florida, USA"Exploring advanced statistical analysis alongside robust research design principles, this rigorous yet accessible text prepares graduate students for practical applications through real-world examples and R/SPSS integration - purpose-built for a full-year curriculum."Michael J. Hautus, Professor of Psychology, University of Auckland, New Zealand“Students of psychological methods must develop a skillset that allows them to design, conduct, and analyze data from research studies with the level of care required to arrive at sound statistical inference.Research Design and Statistical Analysis by Rotello, Myers, Well, and Lorch guides researchers in this journey by discussing the complex topics needed to develop these skills in a comprehensive, yet comprehensible, manner. Their approach uses clear and focused writing to build students’ understanding of concepts ranging from the most basic research designs, descriptive statistics, and hypothesis testing methods to more complex matters, such as repeated-measures designs, the use of covariates, and contrast testing. I also appreciate how the authors introduce students to the use of statistical software packages, such as SPSS and R, in their work. Many textbooks provide code. However, it is much rarer that a textbook balances both computational instruction and the information needed to use statistical software packages in a responsible manner. By exposing students to the assumptions underlying design and analysis choices, as well as common errors in data analysis and inference, Rotello, Myers, Well, and Lorch give researchers insight into the issues they should be considering before making judgments with only a “point-and-click” summary of results. The third edition of the text was my first formal introduction to methods and over 10 years later, it’s still the first book I reach for whether I am double-checking my methodological understanding of a complex topic in my own work or am recommending a must-have reference for an aspiring experimental researcher.”Ryan Guggenmos, Associate Professor of Accounting and Cramer Research Fellow, Darla Moore School of Business, University of South Carolina, USA

• PART 1: Foundations of Research Design and Data Analysis 1. Planning the Research 2. Describing the Data 3. Basic Concepts in Probability 4. Developing the Fundamentals of Hypothesis Testing Using the Binomial Distribution 5. Further Development of the Foundations of Statistical Inference 6. The t Distribution and Its Applications 7. Integrated Analysis I PART 2: Between-Participants Designs 8. Between-Participants Designs: One Factor 9. Multi-Factor Between-Participants Designs 10. Contrasting Means in Between-Subjects Designs 11. Integrated Analysis II PART 3: Repeated-Measures Designs 12. Comparing Experimental Designs and Analyses 13. One-Factor Repeated-Measures Designs 14. Multi-Factor Repeated-Measures and Mixed Designs 15. Nested and Counterbalanced Variables in Repeated-Measures Designs 16. Integrated Analysis III PART 4: Correlation and Regression 17. An Introduction to Correlation and Regression 18. More About Correlation 19. More About Bivariate Regression 20. Introduction to Multiple Regression 21. Inference, Assumptions, and Power in Multiple Regression 22. Additional Topics in Multiple Regression 23. Regression with Qualitative and Quantitative Variables 24. ANCOVA as a Special Case of Multiple Regression 25. Integrated Analysis IV PART 5: Epilogue 26. Some Final Thoughts, Suggestions, and Cautions APPENDICES Appendix A: Notation and Summation Operations Appendix B: Expected Values and Their Applications Appendix C: Statistical TablesAnswers to Selected Exercise