Statistics in Plain English

Description

This introductory textbook provides an inexpensive, brief overview of statistics to help readers gain a better understanding of how statistics work and how to interpret them correctly. Each chapter describes a different statistical technique, ranging from basic concepts like central tendency and describing distributions to more advanced concepts such as "t" tests, regression, repeated measures ANOVA, and factor analysis. Each chapter begins with a short description of the statistic and when it should be used. This is followed by a more in-depth explanation of how the statistic works. Finally, each chapter ends with an example of the statistic in use, and a sample of how the results of analyses using the statistic might be written up for publication. A glossary of statistical terms and symbols is also included. Using the author s own data and examples from published research and the popular media, the book is a straightforward and accessible guide to statistics. New features in the fourth edition include: sets of work problems in each chapter with detailed solutions and additional problems online to help students test their understanding of the material, new ""Work Examples"" to walk students through how to calculate and interpret the statistics featured in each chapter, new examples from the author s own data and from published research and the popular media to help students see how statistics are applied and written about in professional publications, many more examples, tables, and charts to help students visualize key concepts, clarify concepts, and demonstrate how the statistics are used in the real world. a more logical flow, with correlation directly preceding regression, and a combined glossary appearing at the end of the book, a Quick Guide to Statistics, Formulas, and Degrees of Freedom at the start of the book, plainly outlining each statistic and when students should use them, greater emphasis on (and description of) effect size and confidence interval reporting, reflecting their growing importance in research across the social science disciplines an expanded website at www.routledge.com/cw/urdan with PowerPoint presentations, chapter summaries, a new test bank, interactive problems and detailed solutions to the text s work problems, chapter summaries, SPSS datasets for practice, links to useful tools and resources, and videos showing how to calculate statistics, how to calculate and interpret the appendices, and how to understand some of the more confusing tables of output produced by SPSS. " Statistics in Plain English, Fourth Edition" is an ideal guide for statistics, research methods, and/or for courses that use statistics taught at the undergraduate or graduate level, or as a reference tool for anyone interested in refreshing their memory about key statistical concepts. The research examples are from psychology, education, and other social and behavioral sciences. " Cover Half Title Series page Title Page Copyright Page Brief Contents Table of Contents Preface Acknowledgments About the Author Quick Guide to Statistics, Formulas, and Degrees of Freedom 1 Introduction to Social Science Research Principles and Terminology Populations, Samples, Parameters, and Statistics Inferential and Descriptive Statistics Sampling Issues Types of Variables and Scales of Measurement Research Designs Making Sense of Distributions and Graphs Wrapping Up and Looking Forward Work Problems Note 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 Work Problems Note 3 Measures of Variability Range Variance Standard Deviation Measures of Variability in Depth Calculating the Variance and Standard Deviation Why Have Variance? Examples: Examining the Range, Variance, and Standard Deviation Worked Examples Wrapping Up and Looking Forward Work Problems Notes 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 Work Problems 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 Work Problems 6 Standard Errors What Is a Standard Error? Standard Errors in Depth The Conceptual Description of the Standard Error of the Mean 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 Worked Examples Wrapping Up and Looking Forward Work Problems 7 Statistical Significance, Effect Size, and Confidence Intervals Statistical Significance in Depth Samples and Populations Probability Hypothesis Testing and Type I Errors 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 Work Problems Notes 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 Conceptual Issues with the Independent Samples t Test The Standard Error of the Difference between Independent Sample Means Determining the Significance of the t Value for an Independent Samples t Test 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 One-Sample t Test Independent Samples t Test Dependent/Paired t Test Wrapping Up and Looking Forward Work Problems Note 9 One-Way Analysis of Variance ANOVA vs. Independent t Tests One-Way ANOVA in Depth Deciding if the Group Means Are Significantly Different 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 Work Problems Notes 10 Factorial Analysis of Variance When to Use Factorial ANOVA Some Cautions Factorial ANOVA in Depth Main Effects and Controlled or Partial Effects Interactions Interpreting Main Effects in the Presence of an Interaction Effect Testing Simple Effects 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 Work Problems 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 Work Problems 12 Correlation When to Use Correlation and What it Tells Us Pearson Correlation Coefficients in Depth What the Correlation Coefficient Does, and Does Not, Tell Us The Coefficient of Determination Statistically Significant Correlations A Brief Word on Other Types of Correlation Coefficients Point-Biserial Correlation Phi Spearman Rho Example: The Correlation Between Grades and Test Scores Worked Example: Statistical Significance and Confidence Interval Writing it Up Wrapping Up and Looking Forward Work Problems Notes 13 Regression Simple vs. Multiple Regression Variables Used in Regression Regression in Depth Multiple Regression An Example Using SPSS Example: Predicting the Use of Self-Handicapping Strategies Writing it Up Worked Examples Wrapping Up and Looking Forward Work Problems Notes 14 The Chi-Square 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 Work Problems 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 Work Problems Wrapping Up Notes Appendices 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: Critical Values Of The χ2 Distributions Bibliography Glossary of Terms Glossary of Symbols Index