ABSTRACT
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:
- Each section of the book concludes with a chapter that provides an integrated example of how to apply the concepts and procedures covered in the chapters of the section. In addition, the advantages and disadvantages of alternative designs are discussed.
- A new chapter (1) reviews the major steps in planning and executing a study, and the implications of those decisions for subsequent analyses and interpretations.
- A new chapter (13) compares experimental designs to reinforce the connection between design and analysis and to help readers achieve the most efficient research study.
- A new chapter (27) on common errors in data analysis and interpretation.
- Increased emphasis on power analyses to determine sample size using the G*Power 3 program.
- Many new data sets and problems.
- More examples of the use of SPSS (PASW) Version 17, although the analyses exemplified are readily carried out by any of the major statistical software packages.
- A companion website with the data used in the text and the exercises in SPSS and Excel formats; SPSS syntax files for performing analyses; extra material on logistic and multiple regression; technical notes that develop some of the formulas; and a solutions manual and the text figures and tables for instructors only.
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.
TABLE OF CONTENTS
part 1|165 pages
Foundations of Research Design and Data Analysis
chapter Chapter 1|16 pages
Planning the Research
chapter Chapter 2|28 pages
Exploring the Data
chapter Chapter 3|18 pages
Basic Concepts in Probability
chapter Chapter 4|26 pages
Developing the Fundamentals of Hypothesis Testing Using the Binomial Distribution
chapter Chapter 5|33 pages
Further Development of the Foundations of Statistical Inference
chapter Chapter 6|30 pages
The t Distribution and Its Applications
chapter Chapter 7|12 pages
Integrated Analysis I
part 2|140 pages
Between-Subjects Designs
chapter Chapter 8|31 pages
Between-Subjects Designs: One Factor
chapter Chapter 9|38 pages
Multi-Factor Between-Subjects Designs
chapter Chapter 10|33 pages
Contrasting Means in Between-Subjects Designs
chapter Chapter 11|24 pages
Trend Analysis in Between-Subjects Designs
chapter Chapter 12|12 pages
Integrated Analysis II
part 3|126 pages
Repeated-Measures Designs
chapter Chapter 13|23 pages
Comparing Experimental Designs and Analyses
chapter chapter 14|30 pages
One-Factor Repeated-Measures Designs
chapter Chapter 15|35 pages
Multi-Factor Repeated-Measures and Mixed Designs
chapter Chapter 16|22 pages
Nested and Counterbalanced Variables in Repeated-Measures Designs
chapter Chapter 17|14 pages
Integrated Analysis III
part 4|225 pages
Correlation and Regression
chapter chapter 18|32 pages
An Introduction to Correlation and Regression
chapter Chapter 19|26 pages
More About Correlation
chapter Chapter 20|35 pages
More About Bivariate Regression
chapter Chapter 21|23 pages
Introduction to Multiple Regression
chapter Chapter 22|22 pages
Inference, Assumptions, and Power in Multiple Regression
chapter Chapter 23|29 pages
Additional Topics in Multiple Regression
chapter Chapter 24|25 pages
Regression with Qualitative and Quantitative Variables
chapter Chapter 25|20 pages
ANCOVA as a Special Case of Multiple Regression
chapter Chapter 26|11 pages
Integrated Analysis IV
part 5|10 pages
Epilogue