Statistics for People Who (Think They) Hate Statistics Using R - International Student Edition

Neil J. Salkind received his PhD in human development from the University of Maryland, and after teaching for 35 years at the University of Kansas, he was Professor Emeritus in the Department of Psychology and Research in Education, where he collaborated with colleagues and work with students. His early interests were in the area of children's cognitive development, and after research in the areas of cognitive style and (what was then known as) hyperactivity, he was a postdoctoral fellow at the University of North Carolina's Bush Center for Child and Family Policy. His work then changed direction to focus on child and family policy, specifically the impact of alternative forms of public support on various child and family outcomes. He delivered more than 150 professional papers and presentations; written more than 100 trade and textbooks; and is the author of Statistics for People Who (Think They) Hate Statistics (SAGE), Theories of Human Development (SAGE), and Exploring Research (Prentice Hall). He has edited several encyclopedias, including the Encyclopedia of Human Development, the Encyclopedia of Measurement and Statistics, and the Encyclopedia of Research Design. He was editor of Child Development Abstracts and Bibliography for 13 years. He lived in Lawrence, Kansas, where he liked to read, swim with the River City Sharks, work as the proprietor and sole employee of big boy press, bake brownies (see www.statisticsforpeople.com for the recipe), and poke around old Volvos and old houses. Leslie A. Shaw received her PhD in psychology from the University of Kansas, specifically in quantitative psychology. During graduate school, she worked on a variety of projects from university class enrollment, alumni donations, community policing, and self-determination. She also taught statistical computing labs and introductory statistics in a team-teaching format. The self-determination research led to more opportunities at the Beach Center on Disabilities and Kansas University Center on Developmental Disabilities to contribute to research on the Supports Intensity Scale, both adult and child versions, and the Self-Determination Inventory: Self Report. After graduation, she held a postdoctoral position at the Kansas University Center on Developmental Disabilities, where she also taught a class each semester in the quantitative psychology program. She is now a research associate at the Yang-Tan Institute on Employment and Disability in the ILR School at Cornell University. She has coauthored more than 20 articles to date, and she serves as a statistical consultant for the journal Intellectual and Developmental Disabilities.

Preface Acknowledgements About the Authors Part I Yippee! I'm in Statistics Chapter 1. Statistics or Sadistics? It's Up to You What You Will Learn in This Chapter Why Statistics? A 5-Minute History of Statistics Statistics: What it is and Isn't What am I doing in a Statistics Class? Ten Ways to Use this Book (and Learn Statistics at the Same Time) Key to Difficulty Icons Glossary Real-World Stats Summary Time to Practice Part II Welcome to the Interesting, Flexible, Useful, Fun and (Very) Deep Worlds of R and RStudio Chapter 2. Here's Why We Love R and How to Get Started What You Will Learn in This Chapter A Very Short History of R The Plusses of Using R Where to Find and Download R The Opening R Screen A Note About Formatting Bunches of Data - Free! Getting R Help Some Important Lingo RStudio Where to Find RStudio and How to Install It Ordering from RStudio Summary Time to Practice Chapter 3. Using RStudio: Much Easier Than You Think What You Will Learn in This Chapter Why RStudio (and Why Not Just R?) The Grand Tour and All About Those Four Panes RStudio Pane Goodies Showing Your Stuff - Working With Menus and Tabs and A Sample Data Analysis Using RStudio Working with Data Next Step: Using and Importing Datasets Reading in Established Datasets Computing Some Statistics Summary Time to Practice Part III Sigma Freud and Descriptive Statistics Chapter 4. Means to an End: Computing and Understanding Averages What You Will Learn in This Chapter What You Will Learn in This Chapter Computing the Mean Computing the Median Computing the Mode When to Use What Measure of Central Tendency (and All You Need to Know About Scales of Measurement for Now) Using the Computer to Compute Descriptive Statistics Real World Stats Summary Time to Practice Chapter 5. Understanding Variability: Vive la Difference What You Will Learn in This Chapter Why Understanding Variability is Important Computing the Range Computing the Standard Deviation Computing the Variance Using R to Compute Measures of Variability Real World Stats Summary Time to Practice Chapter 6. Creating Graphs: A Picture Really Is Worth a Thousand Words What You Will Learn in This Chapter Why Illustrate Data? Ten Ways to a Great Graphic First Things First: Creating a Frequency Distribution The Plot Thickens: Creating a Histogram The Next Step: A Frequency Polygon Other Cool Ways to Chart Data Using the Computer (R, That Is) to Illustrate Data Real World Stats Summary Time to Practice Chapter 7. Computing Correlation Coefficients: Ice Cream and Crime What You Will Learn in This Chapter What are Correlations All About? Computing a Simple Correlation Coefficient Understanding What the Correlation Coefficient Means A Determined Effort: Squaring the Correlation Coefficient Other Cool Correlations Parting Ways: A Bit About Partial Correlations Summary Time to Practice Chapter 8: Understanding Reliability and Validity: Just the Truth What You Will Learn in This Chapter An Introduction to Reliability and Validity Reliability: Doing it Again Until You Get it Right Different Types of Reliability How Big is Big? Finally: Interpreting Reliability Coefficients Validity: Whoa! What is the Truth? A Last Friendly Word Validity and Reliability: Really Close Cousins Real World Stats Summary Time to Practice Part IV Taking Chances for Fun and Profit Chapter 9. Hypotheticals and You: Testing Your Questions What You Will Learn in This Chapter So You Want to Be a Scientist Samples and Populations The Null Hypothesis The Research Hypothesis What Makes a Good Hypothesis? Real-World Stats Summary Time to Practice Chapter 10. Probability and Why It Counts: Fun with a Bell-Shaped Curve What You'll Learn About in this Chapter Why Probability? The Normal Curve (A.K.A The Bell-Shaped Curve) Our Favorite Standard Score Fat and Skinny Frequency Distributions Real World Stats Summary Time to Practice Part IV Significantly Different: Using Inferential Statistics Chapter 11. Significantly Significant: What It Means for You and Me What You'll Learn About in this Chapter The Concept of Significance Significance Versus Meaningfulness An Introduction to Inferential Statistics An Introduction to Tests of Significance Be Even More Confident Real World Stats Summary Time to Practice 12. The One-Sample Z-Test: Only the Lonely What You'll Learn About in this Chapter Introduction to the One-Sample Z-Test The Path to Wisdom and Knowledge Computing the Z-Test Statistic Using R to Perform a Z-Test Special Effects: Are Those Differences for Real? Real World Stats Summary Time to Practice Chapter 13. t(ea) for Two: Tests Between the Means of Different Groups What You'll Learn About in This Chapter Introduction to the t-test for Independent Samples The Path to Wisdom and Knowledge Computing the t-Test Statistic Using R to Perform a t-Test Real-World Stats Summary Time to Practice 14. t(ea) for Two (Again): Tests Between the Means of Related Groups What You'll Learn About in This Chapter Introduction of the t-Test for Dependent Samples The Path to Wisdom and Knowledge Computing the t-Test Statistic Using R to Perform a t-Test The Effect Size for t(ea) for Two (Again) Real World Stats Summary Time to Practice Chapter 15. Two Groups Too Many? Try Analysis of Variance Introduction to Analysis of Variance The Path to Wisdom and Knowledge Different Flavors of ANOVA Computing the F-test Statistic Using R to Compute the F Ratio The Effect Size for One-Way ANOVA But Where is the Difference? Real World Stats Summary Time to Practice Chapter 16. Two Too Many Factors: Factorial Analysis of Variance-A Brief Introduction What You'll Learn About in This Chapter Introduction to Factorial Analysis of Variance The Path to Wisdom and Knowledge A New Flavor of ANOVA All of These Effects Even More Interesting Interaction Effects Using R to Compute the F Ratio Computing the Effect Size for Factorial ANOVA Real World Stats Summary Time to Practice Chapter 17. Testing Relationships Using the Correlation Coefficient: Cousins or Just Good Friends? What You'll Learn About in This Chapter Introduction to Testing the Correlation Coefficient The Path to Wisdom and Knowledge Computing the Test Statistic Using R to Compute a Correlation Coefficient (Again) Real World Stats Summary Time to Practice 18. Using Linear Regression: Predicting the Future What You'll Learn About in this Chapter Introduction to Linear Regression What is Prediction All About? The Logic of Prediction Drawing the World's Best Line (for Your Data) How Good is Your Prediction? Using R to Compute the Regression Line The More Predictors the Better? Maybe Real World Stats Summary Time to Practice Part VI More Statistics! More Tools! More Fun! Chapter 19. Chi-Square and Some Other Nonparametric Tests: What to Do When You're Not Normal What You'll Learn About in this Chapter Introduction toe Nonparametric Statistics Introduction to the Goodness of Fit (One-Sample) Chi-Square Computing the Goodness of Fit Chi-Square Test Statistic Introduction to the Test of Independence Chi-Square Computing the Test of Independence Chi-Square Test Statistic Using R to Perform Chi-Square Tests Summary Time to Practice 20. Some Other (Important) Statistical Procedures You Should Know About: A Statistical Software Sampler What You'll Learn About in this Chapter Multivariate Analysis of Variance Repeated Measures Analysis of Variance Analysis of Covariance Multiple Regression Multilevel Models Meta-Analysis Logistic Regression Factor Analysis Path Analysis Structural Equation Modeling Summary Appendix A: More Fun Stuff with R and RStudio Appendix B: Tables Appendix C: Data Sets Appendix D: Answers to Practice Questions Appendix E: Math: Just the Basics Appendix F: The Ten (or More) Best (and Most Fun) Internet Sites for Statistics Stuff Appendix G: The Ten Commandments of Data Collection Appendix H: Glossary Appendix I: The Reward Index