Research Design and Statistical Analysis provides comprehensive coverage of the design principles and statistical concepts necessary to make sense of real data. The book’s goal is to provide a strong conceptual foundation to enable readers to generalize concepts to new research situations. Emphasis is placed on the underlying logic and assumptions of the analysis and what it tells the researcher, the limitations of the analysis, and the consequences of violating assumptions. Sampling, design efficiency, and statistical models are emphasized throughout. As per APA recommendations, emphasis is also placed on data exploration, effect size measures, confidence intervals, and using power analyses to determine sample size. "Real-world" data sets are used to illustrate data exploration, analysis, and interpretation. The book offers a rare blend of the underlying statistical assumptions, the consequences of their violations, and practical advice on dealing with them.
Changes in the New Edition:
Part 1 reviews research planning, data exploration, and basic concepts in statistics including sampling, hypothesis testing, measures of effect size, estimators, and confidence intervals. Part 2 presents between-subject designs. The statistical models underlying the analysis of variance for these designs are emphasized, along with the role of expected mean squares in estimating effects of variables, the interpretation of nteractions, and procedures for testing contrasts and controlling error rates. Part 3 focuses on repeated-measures designs and considers the advantages and disadvantages of different mixed designs. Part 4 presents detailed coverage of correlation and bivariate and multiple regression with emphasis on interpretation and common errors, and discusses the usefulness and limitations of these procedures as tools for prediction and for developing theory.
This is one of the few books with coverage sufficient for a 2-semester course sequence in experimental design and statistics as taught in psychology, education, and other behavioral, social, and health sciences. Incorporating the analyses of both experimental and observational data provides continuity of concepts and notation. Prerequisites include courses on basic research methods and statistics. The book is also an excellent resource for practicing researchers.
"This book is written in a clear and comprehensible way. Chapter by chapter the reader gets to know statistics from basic to more advanced level. … There are descriptions and interpretations of statistical concepts and examples of experiments where they can be used. The authors also give the hint on the analysis of experiment. The book is very good lecture about statistics and I read it with a great interest." - Anna Szczepa´nska, Pozna´n University of Life Sciences, Poland, in International Statistical Review
"The authors do an exceptional job in covering important topics in a manner that is sophisticated, rigorous, and yet readily accessible. As in previous editions, the authors lay a solid foundation that allows the reader to easily generalize to situations beyond what is covered. The integrative chapters use real data to show how concepts interrelate – what a wonderful idea. The book continues to be a terrific text book for graduate students as well as a valuable resource book for more experienced researchers." – Edward J. O’Brien, University of New Hampshire, USA
"I love the "integrated analysis" chapters. They will allow students to practice their new skills, to think critically about data sets, and to learn to write results and discussion sections for papers." - Celia M. Klin, Binghamton University, USA
"The Myers & Well book is the best available book for a one-year graduate statistics sequence…I currently use the 2nd edition…I use it because it provides the best fit for the material I think needs to be covered … and it is an outstanding reference that students should have." - William Levine, University of Arkansas, USA
Part 1. Foundations of Research Design and Data Analysis. 1. Planning the Research. 2. Exploring 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-Subjects Designs. 8. Between Subjects Designs: One Factor. 9. Multi-Factor Between-Subjects Designs. 10. Contrasting Means in Between-Subjects Designs. 11. Trend Analysis in Between-Subjects Designs. 12. Integrated Analysis II. Part 3. Repeated-Measures Designs. 13. Comparing Experimental Designs and Analyses. 14. One-Factor Repeated-Measures Designs. 15. Multi-factor Repeated-Measures and Mixed Designs. 16. Nested and Counterbalanced Variables in Repeated-Measures Designs. 17. Integrated Analysis III. Part 4. Correlation and Regression. 18. An Introduction to Correlation and Regression. 19. More about Correlation. 20. More about Bivariate Regression. 21. Introduction to Multiple Regression. 22. Inference, Assumptions, and Power in Multiple Regression. 23. Additional Topics in Multiple Regression. 24. Regression with Qualitative and Quantitative Variables. 25. ANCOVA as a Special Case of Multiple Regression. 26. Integrated Analysis IV: Multiple Regression. Part 5. Epilogue. 27. Some Final Thoughts: Twenty Suggestions and Cautions. Appendixes Appendix A: Notation and Summation Operations. Appendix B: Expected Values and Their Applications. Appendix C: Statistical Tables. Answers to Selected Exercises. References.
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