Statistics in Plain English is a straightforward, conversational introduction to statistics that delivers exactly what its title promises. Each chapter begins with a brief overview of a statistic that describes what the statistic does and when to use it, followed by a detailed step-by-step explanation of how the statistic works and exactly what information it provides. Chapters also include an example of the statistic (or statistics) in use in real-world research, "Worked Examples," "Writing It Up" sections that demonstrate how to write about each statistic, "Wrapping Up and Looking Forward" sections, and practice work problems.
Thoroughly updated throughout, this edition features several key additions and changes. First, a new chapter on person-centered analyses, including cluster analysis and latent class analysis (LCA) has been added, providing an important alternative to the more commonly used variable-centered analyses (e.g., t tests, ANOVA, regression). Next, the chapter on non-parametric statistics has been enhanced with in-depth descriptions of Mann-Whitney U, Kruskall-Wallace, and Wilcoxon Signed-Rank analyses, in addition to the detailed discussion of the Chi-square statistic found in the previous version. These non-parametric statistics are widely used when dealing with non-normally-distributed data. This edition also includes more information about the assumptions of various statistics, including a detailed explanation of the assumptions and consequences of violating the assumptions of regression, as well as more coverage of the normal distribution in statistics. Finally, the book features a multitude of real-world examples throughout to aid student understanding, and provides them with a solid understanding of how inferential statistics work and which additional statistical tools are commonly used by researchers in the social sciences.
Statistics in Plain English is suitable for a wide range of readers, including students taking their first statistics course, professionals who want to refresh their statistical memory, and undergraduate or graduate students who need a concise companion to a more complicated text used in their class. The text works as a standalone or as a supplement and covers a range of statistical concepts from descriptive statistics to factor analysis and person-centered analyses.
Table of Contents
1. Introduction to Social Science Research Principles and Terminology 2. Measures of Central Tendency 3. Measures of Variability 4. The Normal Distribution 5. Standardization and z Scores 6. Standard Errors 7. Statistical Significance, Effect Size, and Confidence Intervals 8. t Tests 9. One-Way Analysis of Variance 10. Factorial Analysis of Variance 11. Repeated-Measures Analysis of Variance 12. Correlation 13. Regression 14. Non-Parametric Statistics 15. Factor Analysis and Reliability Analysis: Data Reduction Techniques 16. Person-Centered Analysis Appendix A. Area Under the Normal Curve Beyond z Appendix B. Critical Values of the t Distributions Appendix C: Critical Values of the F Distributions Appendix D: Critical Values of the Studentized Range Statistic (for Tukey HSD Tests) Appendix E: Fisher’s z Transformations for Correlation Coefficients Appendix F: Critical Values of the Mann-Whitney U Statistic Appendix G: Critical Values for Wilcoxon Signed-Rank Test Appendix H: Critical Values of the X2 Distributions
Timothy C. Urdan is a professor at Santa Clara University. He received his Ph.D. from the Combined Program in Education and Psychology at the University of Michigan, his Master’s degree in Education from Harvard University, and his B.A. in Psychology from University of California, Berkeley. He conducts research on student motivation, classroom contexts, and teacher identity. He serves on the editorial boards of several journals and is the co-editor of the Advances in Motivation and Achievement book series. He is a fellow of the American Psychological Association and lives in Berkeley, California.