Research Design and Statistical Analysis

Auteur

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.

Texte du rabat

This fully updated fourth edition of Research Design and Statistical Analysis provides comprehensive coverage of the design principles and statistical concepts necessary to make sense of real data.

Résumé
This fully updated fourth edition of Research Design and Statistical Analysis provides comprehensive coverage of the design principles and statistical concepts necessary to make sense of real data.

Contenu

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 Tables
            Answers to Selected Exercise