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.

• PrefaceAcknowledgementsAbout the AuthorsPart I Yippee! I’m in StatisticsChapter 1.Statistics or Sadistics? It’s Up to YouWhat You Will Learn in This ChapterWhy Statistics?A 5-Minute History of StatisticsStatistics: What it is and Isn’tWhat am I doing in a Statistics Class?Ten Ways to Use this Book (and Learn Statistics at the Same Time)Key to Difficulty IconsGlossaryReal-World StatsSummaryTime to PracticePart II Welcome to the Interesting, Flexible, Useful, Fun and (Very) Deep Worlds of R and RStudioChapter 2.Here’s Why We Love R and How to Get StartedWhat You Will Learn in This ChapterA Very Short History of RThe Plusses of Using RWhere to Find and Download RThe Opening R ScreenA Note About FormattingBunches of Data – Free!Getting R HelpSome Important LingoRStudioWhere to Find RStudio and How to Install ItOrdering from RStudioSummaryTime to PracticeChapter 3.Using RStudio: Much Easier Than You ThinkWhat You Will Learn in This ChapterWhy RStudio (and Why Not Just R?)The Grand Tour and All About Those Four PanesRStudio Pane GoodiesShowing Your Stuff – Working With Menus and Tabs and A Sample Data Analysis Using RStudioWorking with DataNext Step: Using and Importing DatasetsReading in Established DatasetsComputing Some StatisticsSummaryTime to PracticePart III Sigma Freud and Descriptive StatisticsChapter 4.Means to an End: Computing and Understanding AveragesWhat You Will Learn in This ChapterWhat You Will Learn in This Chapter Computing the MeanComputing the MedianComputing the ModeWhen 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 StatisticsReal World StatsSummaryTime to PracticeChapter 5. Understanding Variability: Vive la DifférenceWhat You Will Learn in This ChapterWhy Understanding Variability is ImportantComputing the RangeComputing the Standard DeviationComputing the VarianceUsing R to Compute Measures of VariabilityReal World StatsSummaryTime to PracticeChapter 6.Creating Graphs: A Picture Really Is Worth a Thousand WordsWhat You Will Learn in This ChapterWhy Illustrate Data?Ten Ways to a Great GraphicFirst Things First: Creating a Frequency DistributionThe Plot Thickens: Creating a HistogramThe Next Step: A Frequency PolygonOther Cool Ways to Chart DataUsing the Computer (R, That Is) to Illustrate DataReal World StatsSummaryTime to PracticeChapter 7.Computing Correlation Coefficients: Ice Cream and CrimeWhat You Will Learn in This ChapterWhat are Correlations All About?Computing a Simple Correlation CoefficientUnderstanding What the Correlation Coefficient MeansA Determined Effort: Squaring the Correlation CoefficientOther Cool CorrelationsParting Ways: A Bit About Partial CorrelationsSummaryTime to PracticeChapter 8: Understanding Reliability and Validity: Just the TruthWhat You Will Learn in This ChapterAn Introduction to Reliability and ValidityReliability: Doing it Again Until You Get it RightDifferent Types of ReliabilityHow Big is Big? Finally: Interpreting Reliability CoefficientsValidity: Whoa! What is the Truth?A Last Friendly WordValidity and Reliability: Really Close CousinsReal World StatsSummaryTime to PracticePart IV Taking Chances for Fun and ProfitChapter 9.Hypotheticals and You: Testing Your QuestionsWhat You Will Learn in This ChapterSo You Want to Be a ScientistSamples and PopulationsThe Null HypothesisThe Research HypothesisWhat Makes a Good Hypothesis?Real-World StatsSummaryTime to PracticeChapter 10.Probability and Why It Counts: Fun with a Bell-Shaped CurveWhat You’ll Learn About in this ChapterWhy Probability?The Normal Curve (A.K.A The Bell-Shaped Curve)Our Favorite Standard ScoreFat and Skinny Frequency DistributionsReal World StatsSummaryTime to PracticePart IV Significantly Different: Using Inferential StatisticsChapter 11.Significantly Significant: What It Means for You and MeWhat You’ll Learn About in this ChapterThe Concept of SignificanceSignificance Versus MeaningfulnessAn Introduction to Inferential StatisticsAn Introduction to Tests of SignificanceBe Even More ConfidentReal World StatsSummaryTime to Practice12. The One-Sample Z-Test: Only the LonelyWhat You’ll Learn About in this ChapterIntroduction to the One-Sample Z-TestThe Path to Wisdom and KnowledgeComputing the Z-Test StatisticUsing R to Perform a Z-TestSpecial Effects: Are Those Differences for Real?Real World StatsSummaryTime to PracticeChapter 13.t(ea) for Two: Tests Between the Means of Different GroupsWhat You’ll Learn About in This ChapterIntroduction to the t-test for Independent SamplesThe Path to Wisdom and KnowledgeComputing the t-Test StatisticUsing R to Perform a t-TestReal-World StatsSummaryTime to Practice14.t(ea) for Two (Again): Tests Between the Means of Related GroupsWhat You’ll Learn About in This ChapterIntroduction of the t-Test for Dependent SamplesThe Path to Wisdom and KnowledgeComputing the t-Test StatisticUsing R to Perform a t-TestThe Effect Size for t(ea) for Two (Again)Real World StatsSummaryTime to PracticeChapter 15.Two Groups Too Many? Try Analysis of VarianceIntroduction to Analysis of VarianceThe Path to Wisdom and KnowledgeDifferent Flavors of ANOVAComputing the F-test StatisticUsing R to Compute the F RatioThe Effect Size for One-Way ANOVABut Where is the Difference?Real World StatsSummaryTime to PracticeChapter 16.Two Too Many Factors: Factorial Analysis of Variance—A Brief IntroductionWhat You’ll Learn About in This ChapterIntroduction to Factorial Analysis of VarianceThe Path to Wisdom and KnowledgeA New Flavor of ANOVAAll of These EffectsEven More Interesting Interaction EffectsUsing R to Compute the F RatioComputing the Effect Size for Factorial ANOVAReal World StatsSummaryTime to PracticeChapter 17.Testing Relationships Using the Correlation Coefficient: Cousins or Just Good Friends?What You’ll Learn About in This ChapterIntroduction to Testing the Correlation CoefficientThe Path to Wisdom and KnowledgeComputing the Test StatisticUsing R to Compute a Correlation Coefficient (Again)Real World StatsSummaryTime to Practice18.Using Linear Regression: Predicting the FutureWhat You’ll Learn About in this ChapterIntroduction to Linear RegressionWhat is Prediction All About?The Logic of PredictionDrawing the World’s Best Line (for Your Data)How Good is Your Prediction?Using R to Compute the Regression LineThe More Predictors the Better? MaybeReal World StatsSummaryTime to PracticePart VI More Statistics! More Tools! More Fun!Chapter 19. Chi-Square and Some Other Nonparametric Tests: What to Do When You’re Not NormalWhat You’ll Learn About in this ChapterIntroduction toe Nonparametric StatisticsIntroduction to the Goodness of Fit (One-Sample) Chi-SquareComputing the Goodness of Fit Chi-Square Test StatisticIntroduction to the Test of Independence Chi-SquareComputing the Test of Independence Chi-Square Test StatisticUsing R to Perform Chi-Square TestsSummaryTime to Practice20.Some Other (Important) Statistical Procedures You Should Know About: A Statistical Software SamplerWhat You’ll Learn About in this ChapterMultivariate Analysis of VarianceRepeated Measures Analysis of VarianceAnalysis of CovarianceMultiple RegressionMultilevel ModelsMeta-AnalysisLogistic RegressionFactor AnalysisPath AnalysisStructural Equation ModelingSummaryAppendix A: More Fun Stuff with R and RStudioAppendix B: TablesAppendix C: Data SetsAppendix D: Answers to Practice QuestionsAppendix E: Math: Just the BasicsAppendix F: The Ten (or More) Best (and Most Fun) Internet Sites for Statistics StuffAppendix G: The Ten Commandments of Data CollectionAppendix H: GlossaryAppendix I: The RewardIndex