Statistics in Plain English

Description

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 (or set of statistics) 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) used 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, Kruskal-Wallis, and Wilcoxon Signed-Rank analyses, in addition to the detailed discussion of the Chi-square statistic found in the previous edition. These nonparametric statistics are widely used when dealing with nonnormally 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 several statistics techniques commonly used by researchers in the social sciences work. 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. Cover Half Title Title Page Copyright Page Contents Preface Acknowledgments About the Author Quick Guide to Statistics, Formulas, and Degrees of Freedom Chapter 1: Introduction to Social Science Research Principles and Terminology The Importance of Statistics and Research in Our Lives Terminology Used in Statistics and Research Methods Making Sense of Distributions and Graphs Wrapping Up and Looking Forward Chapter 2: Measures of Central Tendency Measures of Central Tendency in Depth Example: The Mean, Median, and Mode of Skewed Distributions Writing It Up Wrapping Up and Looking Forward Chapter 3: Measures of Variability Range Variance Standard Deviation Measures of Variability in Depth Examples: Examining the Range, Variance, and Standard Deviation Worked Examples Wrapping Up and Looking Forward Chapter 4: The Normal Distribution Characteristics of the Normal Distribution Why Is the Normal Distribution So Important? The Normal Distribution in Depth The Relationship between the Sampling Method and the Normal Distribution Skew and Kurtosis Example 1: Applying Normal Distribution Probabilities to a Normal Distribution Example 2: Applying Normal Distribution Probabilities to a Nonnormal Distribution Wrapping Up and Looking Forward Chapter 5: Standardization and z Scores Standardization and z Scores in Depth Interpreting z Scores Examples: Comparing Raw Scores and z Scores Worked Examples Wrapping Up and Looking Forward Chapter 6: Standard Errors What Is a Standard Error? Standard Errors in Depth How to Calculate the Standard Error of the Mean The Central Limit Theorem The Normal Distribution and t Distributions: Comparing z Scores and t Values The Use of Standard Errors in Inferential Statistics Example: Sample Size and Standard Deviation Effects on the Standard Error of the Mean Worked Examples Wrapping Up and Looking Forward Chapter 7: Statistical Significance, Effect Size, and Confidence Intervals Statistical Significance in Depth Assumptions of Parametric Tests Limitations of Statistical Significance Testing Effect Size in Depth Confidence Intervals in Depth Example: Statistical Significance, Confidence Interval, and Effect Size for a One-Sample t Test of Motivation Wrapping Up and Looking Forward Chapter 8: t Tests What Is a t Test? t Distributions The One-Sample t Test The Independent Samples t Test Dependent (Paired) Samples t Test Independent Samples t Tests in Depth Paired or Dependent Samples t Tests in Depth Example 1: Comparing Boys’ and Girls’ Grade Point Averages Example 2: Comparing Fifth- and Sixth-Grade GPAs Writing It Up Worked Examples Wrapping Up and Looking Forward Chapter 9: One-Way Analysis of Variance ANOVA vs. Independent t Tests One-Way ANOVA in Depth Post-Hoc Tests Effect Size Example: Comparing the Sleep of 5-, 8-, and 12-Year-Olds Writing It Up Worked Example Wrapping Up and Looking Forward Chapter 10: Factorial Analysis of Variance When to Use Factorial ANOVA Some Cautions Factorial ANOVA in Depth Analysis of Covariance Illustration of Factorial ANOVA, ANCOVA, and Effect Size with Real Data Example: Performance, Choice, and Public vs. Private Evaluation Writing It Up Wrapping Up and Looking Forward Chapter 11: Repeated-Measures Analysis of Variance When to Use Each Type of Repeated-Measures Technique Repeated-Measures ANOVA in Depth Repeated-Measures Analysis of Covariance (ANCOVA) Adding an Independent Group Variable Example: Changing Attitudes about Standardized Tests Writing It Up Wrapping Up and Looking Forward Chapter 12: Correlation When to Use Correlation and What It Tells Us Pearson Correlation Coefficients in Depth Comparing Correlation Coefficients and Calculating Confidence Intervals: The Fisher’s Z Transformation A Brief Word on Other Types of Correlation Coefficients Example: The Correlation Between Grades and Test Scores Worked Example: Statistical Significance of a Sample Correlation Coefficient Writing It Up Wrapping Up and Looking Forward Chapter 13: Regression Simple vs. Multiple Regression Regression in Depth Multiple Regression Example: Predicting the Use of Self-Handicapping Strategies Writing It Up Worked Examples Wrapping Up and Looking Forward Chapter 14: Nonparametric Statistics Mann-Whitney U Mann-Whitney U Test in Depth Wilcoxon Signed-Rank Test Wilcoxon Signed-Rank Test in Depth Kruskal-Wallis Test Chi-square (χ2) Test of Independence Chi-Square Test of Independence in Depth Example: Generational Status and Grade Level Writing It Up Worked Example Wrapping Up and Looking Forward Chapter 15: Factor Analysis and Reliability Analysis: Data Reduction Techniques Factor Analysis in Depth A More Concrete Example of Exploratory Factor Analysis Confirmatory Factor Analysis: A Brief Introduction Reliability Analysis in Depth Writing It Up Wrapping Up and Looking Forward Chapter 16: Person-Centered Analysis Cluster Analysis Cluster Analysis in Depth Latent Class Analysis Latent Class Analysis in Depth Writing It Up Wrapping Up and Looking Forward 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: Table of the Fischer’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 Bibliography Glossary of Symbols Glossary of Terms Index