Discovering Statistics Using R

Andy Field is Professor of Quantitative Methods at the University of Sussex. He has published widely (100+ research papers, 29 book chapters, and 17 books in various editions) in the areas of child anxiety and psychological methods and statistics. His current research interests focus on barriers to learning mathematics and statistics.He is internationally known as a statistics educator. He has written several widely used statistics textbooks including Discovering Statistics Using IBM SPSS Statistics (winner of the 2007 British Psychological Society book award), Discovering Statistics Using R, and An Adventure in Statistics (shortlisted for the British Psychological Society book award, 2017; British Book Design and Production Awards, primary, secondary and tertiary education category, 2016; and the Association of Learned & Professional Society Publishers Award for innovation in publishing, 2016), which teaches statistics through a fictional narrative and uses graphic novel elements. He has also written the adventr and discovr packages for the statistics software R that teach statistics and R through interactive tutorials.His uncontrollable enthusiasm for teaching statistics to psychologists has led to teaching awards from the University of Sussex (2001, 2015, 2016, 2018, 2019), the British Psychological Society (2006) and a prestigious UK National Teaching fellowship (2010).He′s done the usual academic things: had grants, been on editorial boards, done lots of admin/service but he finds it tedious trying to remember this stuff. None of them matter anyway because in the unlikely event that you′ve ever heard of him it′ll be as the ′Stats book guy′. In his spare time, he plays the drums very noisily in a heavy metal band, and walks his cocker spaniel, both of which he finds therapeutic.Jeremy Miles, RAND Corporation, USA. Zoë Field, University of Sussex, UK

In statistics, R is the way of the future. The big boys and girls have known this for some time: There are now millions of R users in academia and industry. R is free (as in no cost) and free (as in speech). Andy, Jeremy, and Zoe′s book now makes R accessible to the little boys and girls like me and my students. Soon all classes in statistics will be taught in R.I have been teaching R to psychologists for several years and so I have been waiting for this book for some time. The book is excellent, and it is now the course text for all my statistics classes. I′m pretty sure the book provides all you need to go from statistical novice to working researcher.Take, for example, the chapter on t-tests. The chapter explains how to compare the means of two groups from scratch. It explains the logic behind the tests, it explains how to do the tests in R with a complete worked example, which papers to read in the unlikely event you do need to go further, and it explains what you need to write in your practical report or paper. But it also goes further, and explains how t-tests and regression are related---and are really the same thing---as part of the general linear model. So this book offers not just the step-by-step guidance needed to complete a particular test, but it also offers the chance to reach the zen state of total statistical understanding.Prof. Neil StewartWarwick University Field′s Discovering Statistics is popular with students for making a sometimes deemed inaccessible topic accessible, in a fun way. In Discovering Statistics Using R, the authors have managed to do this using a statistics package that is known to be powerful, but sometimes deemed just as inaccessible to the uninitiated, all the while staying true to Field′s off-kilter approach. Dr Marcel van EgmondUniversity of Amsterdam Probably the wittiest and most amusing of the lot (no, really), this book takes yet another approach: it is 958 pages of R-based stats wisdom (plus online accoutrements)... A thoroughly engaging, expansive, thoughtful and complete guide to modern statistics. Self-deprecating stories lighten the tone, and the undergrad-orientated ′stupid faces′ (Brian Haemorrhage, Jane Superbrain, Oliver Twisted, etc.) soon stop feeling like a gimmick, and help to break up the text with useful snippets of stats wisdom. It is very mch a student textbook but it is brilliant... Field et al. is the complete package.David M. ShukerAnimJournal of Animal Behaviour

• Why Is My Evil Lecturer Forcing Me to Learn Statistics?What will this chapter tell me?What the hell am I doing here? I don′t belong hereInitial observation: finding something that needs explainingGenerating theories and testing themData collection 1: what to measureData collection 2: how to measureAnalysing dataWhat have I discovered about statistics?Key terms that I′ve discoveredSmart Alex′s tasksFurther readingInteresting real researchEverything You Ever Wanted to Know About Statistics (Well, Sort of)What will this chapter tell me?Building statistical modelsPopulations and samplesSimple statistical modelsGoing beyond the dataUsing statistical models to test research questionsWhat have I discovered about statistics?Key terms that I′ve discoveredSmart Alex′s tasksFurther readingInteresting real researchThe R EnvironmentWhat will this chapter tell me?Before you startGetting startedUsing RGetting data into REntering data with R CommanderUsing other software to enter and edit dataSaving DataManipulating DataWhat have I discovered about statistics?R Packages Used in This ChapterR Functions Used in This ChapterKey terms that I′ve discoveredSmart Alex′s TasksFurther readingExploring Data with GraphsWhat will this chapter tell me?The art of presenting dataPackages used in this chapterIntroducing ggplot2Graphing relationships: the scatterplotHistograms: a good way to spot obvious problemsBoxplots (box-whisker diagrams)Density plotsGraphing meansThemes and optionsWhat have I discovered about statistics?R packages used in this chapterR functions used in this chapterKey terms that I′ve discoveredSmart Alex′s tasksFurther readingInteresting real researchExploring AssumptionsWhat will this chapter tell me?What are assumptions?Assumptions of parametric dataPackages used in this chapterThe assumption of normalityTesting whether a distribution is normalTesting for homogeneity of varianceCorrecting problems in the dataWhat have I discovered about statistics?R packages used in this chapterR functions used in this chapterKey terms that I′ve discoveredSmart Alex′s tasksFurther readingCorrelationWhat will this chapter tell me?Looking at relationshipsHow do we measure relationships?Data entry for correlation analysisBivariate correlationPartial correlationComparing correlationsCalculating the effect sizeHow to report correlation coefficentsWhat have I discovered about statistics?R packages used in this chapterR functions used in this chapterRegressionWhat will this chapter tell me?An Introduction to regressionPackages used in this chapterGeneral procedure for regression in RInterpreting a simple regressionMultiple regression: the basicsHow accurate is my regression model?How to do multiple regression using R Commander and RTesting the accuracy of your regression modelRobust regression: bootstrappingHow to report multiple regressionCategorical predictors and multiple regressionWhat have I discovered about statistics?R packages used in this chapterR functions used in this chapterKey terms that I′ve discoveredSmart Alex′s tasksFurther readingInteresting real researchLogistic RegressionWhat will this chapter tell me?Background to logistic regressionWhat are the principles behind logistic regression? Assumptions and things that can go wrongPackages used in this chapterBinary logistic regression: an example that will make you feel eelHow to report logistic regressionTesting assumptions: another examplePredicting several categories: multinomial logistic regressionWhat have I discovered about statistics?R packages used in this chapterR functions used in this chapterKey terms that I′ve discoveredSmart Alex′s tasksFurther readingInteresting real researchComparing Two MeansWhat will this chapter tell me?Packages used in this chapterLooking at differencesThe t-testThe independent t-testThe dependent t-testBetween groups or repeated measures?What have I discovered about statistics?R packages used in this chapterR functions used in this chapterKey terms that I′ve discoveredSmart Alex′s tasksFurther readingInteresting real researchComparing Several Means: ANOVA (GLM 1)What will this chapter tell me?The theory behind ANOVAAssumptions of ANOVAPlanned contrastsPost hoc proceduresOne-way ANOVA using RCalculating the effect sizeReporting results from one-way independent ANOVAWhat have I discovered about statistics?R packages used in this chapterR functions used in this chapterKey terms that I′ve discoveredSmart Alex′s tasksFurther readingInteresting real researchAnalysis of Covariance, ANCOVA (GLM 2)What will this chapter tell me?What is ANCOVA?Assumptions and issues in ANCOVAANCOVA using RRobust ANCOVACalculating the effect sizeReporting resultsWhat have I discovered about statistics?R packages used in this chapterR functions used in this chapterKey terms that I′ve discoveredSmart Alex′s tasksFurther readingInteresting real researchFactorial ANOVA (GLM 3)What will this chapter tell me?Theory of factorial ANOVA (independant design)Factorial ANOVA as regressionTwo-Way ANOVA: Behind the scenesFactorial ANOVA using RInterpreting interaction graphsRobust factorial ANOVACalculating effect sizesReporting the results of two-way ANOVAWhat have I discovered about statistics?R packages used in this chapterR functions used in this chapterKey terms that I′ve discoveredSmart Alex′s tasksFurther readingInteresting real researchRepeated-Measures Designs (GLM 4)What will this chapter tell me?Introduction to repeated-measures designsTheory of one-way repeated-measures ANOVAOne-way repeated measures designs using REffect sizes for repeated measures designsReporting one-way repeated measures designsFactorisal repeated measures designsEffect Sizes for factorial repeated measures designsReporting the results from factorial repeated measures designsWhat have I discovered about statistics?R packages used in this chapterR functions used in this chapterKey terms that I′ve discoveredSmart Alex′s tasksFurther readingInteresting real researchMixed Designs (GLM 5)What will this chapter tell me?Mixed designsWhat do men and women look for in a partner?Entering and exploring your dataMixed ANOVAMixed designs as a GLMCalculating effect sizesReporting the results of mixed ANOVARobust analysis for mixed designsWhat have I discovered about statistics?R packages used in this chapterR functions used in this chapterKey terms that I′ve discoveredSmart Alex′s tasksFurther readingInteresting real researchNon-Parametric TestsWhat will this chapter tell me?When to use non-parametric testsPackages used in this chapterComparing two independent conditions: the Wilcoxon rank-sum testComparing two related conditions: the Wilcoxon signed-rank testDifferences between several independent groups: the Kruskal-Wallis testDifferences between several related groups: Friedman′s ANOVAWhat have I discovered about statistics?R packages used in this chapterR functions used in this chapterKey terms that I′ve discoveredSmart Alex′s tasksFurther readingInteresting real researchMultivariate Analysis of Variance (MANOVA)What will this chapter tell me?When to use MANOVAIntroduction: similarities and differences to ANOVATheory of MANOVAPractical issues when conducting MANOVAMANOVA using RRobust MANOVAReporting results from MANOVAFollowing up MANOVA with discriminant analysisReporting results from discriminant analysisSome final remarksWhat have I discovered about statistics?R packages used in this chapterR functions used in this chapterKey terms that I′ve discoveredSmart Alex′s tasksFurther readingInteresting real researchExploratory Factor AnalysisWhat will this chapter tell me?When to use factor analysisFactorsResearch exampleRunning the analysis with R CommanderRunning the analysis with RFactor scoresHow to report factor analysisReliability analysisReporting reliability analysisWhat have I discovered about statistics?R Packages Used in This ChapterR Functions Used in This ChapterKey terms that I′ve discoveredSmart Alex′s tasksFurther readingInteresting real researchCategorical DataWhat will this chapter tell me?Packages used in this chapterAnalysing categorical dataTheory of Analysing Categorical DataAssumptions of the chi-square testDoing the chi-square test using RSeveral categorical variables: loglinear analysisAssumptions in loglinear analysisLoglinear analysis using RFollowing up loglinear analysisEffect sizes in loglinear analysisReporting the results of loglinear analysisWhat have I discovered about statistics?R packages used in this chapter R functions used in this chapterKey terms that I′ve discoveredSmart Alex′s tasksFurther readingInteresting real researchMultilevel Linear ModelsWhat will this chapter tell me?Hierarchical dataTheory of multilevel linear modelsThe multilevel modelSome practical issuesMultilevel modelling on RGrowth modelsHow to report a multilevel modelWhat have I discovered about statistics?R packages used in this chapter R functions used in this chapterKey terms that I′ve discoveredSmart Alex′s tasksFurther readingInteresting real researchEpilogue: Life After Discovering StatisticsTroubleshooting RGlossaryAppendixTable of the standard normal distributionCritical Values of the t-DistributionCritical Values of the F-DistributionCritical Values of the chi-square DistributionReferences