Behavioral Research Data Analysis with R PDF
Preview Behavioral Research Data Analysis with R
Use R! SeriesEditors: RobertGentleman KurtHornik GiovanniParmigiani Forfurthervolumes: http://www.springer.com/series/6991 Use R! Albert:BayesianComputationwithR Bivand/Pebesma/Go´mez-Rubio:AppliedSpatialDataAnalysiswithR Cook/Swayne:InteractiveandDynamicGraphicsforDataAnalysis: WithRandGGobi Hahne/Huber/Gentleman/Falcon:BioconductorCaseStudies Paradis:AnalysisofPhylogeneticsandEvolutionwithR Pfaff:AnalysisofIntegratedandCointegratedTimeSerieswithR Sarkar:Lattice:MultivariateDataVisualizationwithR Spector:DataManipulationwithR Yuelin Li • Jonathan Baron Behavioral Research Data Analysis with R 123 YuelinLi JonathanBaron MemorialSloan-KetteringCancerCenter DepartmentofPsychology DepartmentofPsychiatryandBehavioral UniversityofPennsylvania Sciences 3720WalnutStreet 641LexingtonAve.7thFloor Philadelphia,Pennsylvania19104-6241 NewYork,NewYork10022-4503 USA USA [email protected] [email protected] SeriesEditors: RobertGentleman KurtHornik PrograminComputationalBiology Departmentfu¨rStatistikundMathematik DivisionofPublicHealthSciences Wirtschaftsuniversita¨tWienAugasse2-6 FredHutchinsonCancerResearchCenter A-1090Wien 1100FairviewAve.N,M2-B876 Austria Seattle,Washington98109-1024 USA GiovanniParmigiani TheSidneyKimmelComprehensiveCancer CenteratJohnsHopkinsUniversity 550NorthBroadway Baltimore,MD21205-2011 USA ISBN978-1-4614-1237-3 e-ISBN978-1-4614-1238-0 DOI10.1007/978-1-4614-1238-0 SpringerNewYorkDordrechtHeidelbergLondon LibraryofCongressControlNumber:2011940221 ©SpringerScience+BusinessMedia,LLC2012 Allrightsreserved.Thisworkmaynotbetranslatedorcopiedinwholeorinpartwithoutthewritten permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY10013, USA),except forbrief excerpts inconnection with reviews orscholarly analysis. Usein connectionwithanyformofinformationstorageandretrieval,electronicadaptation,computersoftware, orbysimilarordissimilarmethodologynowknownorhereafterdevelopedisforbidden. Theuseinthispublicationoftradenames,trademarks,servicemarks,andsimilarterms,eveniftheyare notidentifiedassuch,isnottobetakenasanexpressionofopinionastowhetherornottheyaresubject toproprietaryrights. Printedonacid-freepaper SpringerispartofSpringerScience+BusinessMedia(www.springer.com) Preface ThisbookiswrittenforbehavioralscientistswhowanttoconsideraddingRtotheir existingsetofstatisticaltools,orwanttoswitchtoRastheirmaincomputationtool. We aimprimarilytohelppractionersofbehavioralresearchmakethetransitionto R. The focus is to providepractical advice on some of the widely used statistical methods in behavioral research, using a set of notes and annotated examples. We alsoaimtohelpbeginnerslearnmoreaboutstatisticsandbehavioralresearch.These arestatisticaltechniquesusedbypsychologistswhodoresearchonhumansubjects, but of course they are also relevant to researchers in others fields that do similar kindsofresearch. WeassumethatthereaderhasreadtherelevantpartsofRmanualsontheCRAN websiteathttp://www.r-project.org,suchas“AnIntroductiontoR”,“R DataImport/Export”,and“RInstallationandAdministration”.Weassumethatthe reader has gotten to the point of installing R and trying a couple of examples. We also assume that the reader has relevant experiences in using other statistical packagestocarryoutdataanalytictaskscoveredinthisbook.Thesourcecodeand data for some of the examples in the book can be downloaded from the book’s website at: http://idecide.mskcc.org/yl home/rbook/. We do not dwellonthestatisticaltheoriesunlesssomedetailsareessentialintheappropriate use of the statistical methods. When they are called for, theoretical details are accompaniedbyvisualexplanationswheneverfeasible.Mathematicalequationsare usedthroughoutthebookinthehopesthatreaderwillfindthemhelpfulingeneral, and specifically in reaching beyond the scope of this book. For example, matrix notationsareusedinthechapterscoveringlinearregressionandlinearmixed-effects modelingbecausetheyarethestandardnotationsfoundinstatisticsjournals.Abasic appreciationofmathematicalnotationsmayhelpthereadersimplementthesenew techniquesbeforeapackagedsolutionisavailable.Nevertheless,themainemphasis of this book is on the practical data analytic skills so that they can be quickly incorporatedintothereader’sownresearch. Thestatisticaltechniquesinthisbookrepresentmanyofstatisticaltechniquesin ourownresearch.Thepedagogicalplanistopresentstraightforwardsolutionsand add more sophisticated techniques if they help improve clarity and/or efficiency. v vi Preface As can be seen in the first example in Chap. 1, the same analysis can be carried outbyastraightforwardandamoresophisticatedmethod.Chapters1–4coverbasic topicssuchasdataimport/export,statisticalmethodsforcomparingmeansandpro- portions,andgraphics.Thesetopicsmaybepartofanintroductorytextforstudents in behavioral sciences. Data analysis can often be adequately addressed with no more than these straightforward methods. Chapter 4 contains plots in published articles in the journal Judgment and Decision Making (http://journal.sjdm.org/). Chapters5–7 covertopicswith intermediarydifficulty,such as repeated-measures ANOVA,ordinaryleastsquareregression,logisticregression,andstatisticalpower andsamplesizeconsiderations.Thesetopicsaretypicallytaughtatamoreadvanced undergraduatelevelorfirstyeargraduatelevel. Practitioners of behavioral statistics are often asked to estimate the statistical power of a study design. R provides a set of flexible functions for sample size estimation. More complex study designs may involve estimating statistical power bysimulations.WefinditeasiertodosimulationswithRthanwithotherstatistical packagesweknow.ExamplesareprovidedinChaps.7and11. Theremainderofthisbookcovermoreadvancedtopics.Chapter8coversItem ResponseTheory(IRT),astatisticalmethodusedinthedevelopmentandvalidation of psychologicaland educationalassessment tools.We beginChap.8 with simple examplesandendwithsophisticatedapplicationsthatrequireaBayesianapproach. Suchtopicscaneasilytakeupafullvolume.Onlypracticalanalytictasksarecov- eredsothatthereadercanquicklyadaptourexamplesforhisorherownresearch. ThelatentregressionRaschmodelinSect.8.4.2highlightsthepowerandflexibility of R in working with other statistical languages such as WinBUGS/OpenBUGS. Chapter9coversmissingdataimputation.Chapters10–11coverhierarchicallinear models applied in repeated-measured data and clustered data. These topics are written for researchers already familiar with the theories. Again, these chapters emphasizethepracticaldataanalysisskillsandnotthetheories. R evolves continuously. New techniques and user-contributed packages are constantly evolving. We strive to provide the latest techniques. However, readers should consult other sources for a fuller understanding of relevant topics. The R journalpublishesthelatesttechniquesandnewpackages.Anothergoodsourcefor new techniquesis The Journalof Statistical Software (http://www.jstatsoft.org/). The R-help mailing list is another indispensable resource. User contributions make R a truly collaborative statistical computation framework.Many great texts and tutorials for beginners and intermediate users are already widely available. Beginner-level tutorials and how-to guides can be found online at the CRAN “ContributedDocumentation”page. Thisbookoriginatedfromouronlinetutorial“NotesontheuseofRforpsychol- ogy experiments and questionnaires.” Many individuals facilitated the transition. Wewouldliketothankthemformakingthisbookpossible.JohnKimmel,former editorforthisbookatSpringer,firstencouragedustowritethisbookandprovided continuousguidanceandencouragement.SpecialthanksgotoKathrynSchelland Marc Strauss and other editorial staff at Springer on the preparation of the book. Severalannonymousreviewersprovidedsuggestionsonhowtoimprovethebook. Preface vii Weareespeciallyindebtedtotheindividualswhohelpedsupplythedatausedinthe examples,includingtheauthorsoftheRpackagesweuse,andthosewhomakethe rawdatafreelyaccessibleonline. NewYork YuelinLi Philadelphia JonathanBaron Contents 1 Introduction ............................................................... 1 1.1 AnExampleRSession .............................................. 1 1.2 AFewUsefulConceptsandCommands............................ 3 1.2.1 Concepts...................................................... 3 1.2.2 Commands.................................................... 4 1.3 DataObjectsandDataTypes........................................ 9 1.3.1 VectorsofCharacterStrings................................. 10 1.3.2 Matrices,Lists,andDataFrames............................ 12 1.4 FunctionsandDebugging ........................................... 15 2 ReadingandTransformingDataFormat .............................. 19 2.1 ReadingandTransformingData .................................... 19 2.1.1 DataLayout................................................... 19 2.1.2 ASimpleQuestionnaireExample........................... 19 2.1.3 OtherWaystoReadinData ................................. 25 2.1.4 OtherWaystoTransformVariables......................... 26 2.1.5 UsingRtoComputeCourseGrades ........................ 30 2.2 ReshapeandMergeDataFrames ................................... 31 2.3 DataManagementwithaSQLDatabase ........................... 33 2.4 SQLDatabaseConsiderations....................................... 35 3 StatisticsforComparingMeansandProportions..................... 39 3.1 ComparingMeansofContinuousVariables........................ 39 3.2 MoreonManualCheckingofData ................................. 42 3.3 ComparingSampleProportions..................................... 43 3.4 ModeratingEffectinloglin()................................... 45 3.5 AssessingChangeofCorrelatedProportions....................... 49 3.5.1 McNemarTestAcrossTwoSamples........................ 50 4 RGraphicsandTrellisPlots............................................. 55 4.1 DefaultBehaviorofBasicCommands.............................. 55 4.2 OtherGraphics....................................................... 56 ix x Contents 4.3 SavingGraphics...................................................... 56 4.4 MultipleFiguresonOneScreen..................................... 57 4.5 OtherGraphicsTricks ............................................... 57 4.6 ExamplesofSimpleGraphsinPublications........................ 58 4.6.1 http://journal.sjdm.org/8827/ jdm8827.pdf.............................................. 60 4.6.2 http://journal.sjdm.org/8814/ jdm8814.pdf.............................................. 63 4.6.3 http://journal.sjdm.org/8801/ jdm8801.pdf.............................................. 64 4.6.4 http://journal.sjdm.org/8319/ jdm8319.pdf.............................................. 65 4.6.5 http://journal.sjdm.org/8221/ jdm8221.pdf.............................................. 66 4.6.6 http://journal.sjdm.org/8210/ jdm8210.pdf.............................................. 68 4.7 ShadedAreasUnderaCurve........................................ 69 4.7.1 Vectorsinpolygon() ..................................... 71 4.8 LatticeGraphics...................................................... 72 5 AnalysisofVariance:Repeated-Measures ............................. 79 5.1 Example1:TwoWithin-SubjectFactors ........................... 79 5.1.1 UnbalancedDesigns ......................................... 83 5.2 Example2:MaxwellandDelaney .................................. 85 5.3 Example3:MoreThanTwoWithin-SubjectFactors .............. 88 5.4 Example 4: A Simpler Design with Only One Within-SubjectVariable ............................................. 89 5.5 Example5:OneBetween,TwoWithin ............................. 89 5.6 OtherUsefulFunctionsforANOVA................................ 91 5.7 GraphicswithErrorBars............................................ 93 5.8 AnotherWaytodoErrorBarsUsingplotCI()...................... 95 5.8.1 UseError()forRepeated-MeasureANOVA............ 96 5.8.2 Sphericity..................................................... 102 5.9 HowtoEstimatetheGreenhouse–GeisserEpsilon? ............... 103 5.9.1 Huynh–FeldtCorrection..................................... 105 6 LinearandLogisticRegression.......................................... 109 6.1 LinearRegression.................................................... 109 6.2 AnApplicationofLinearRegressiononDiamondPricing........ 110 6.2.1 PlottingDataBeforeModelFitting ......................... 111 6.2.2 CheckingModelDistributionalAssumptions............... 114 6.2.3 AssessingModelFit.......................................... 115 6.3 LogisticRegression.................................................. 118 6.4 Log–LinearModels.................................................. 119 6.5 RegressioninVector–MatrixNotation.............................. 120 6.6 CautiononModelOverfitandClassificationErrors ............... 122
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