Modern statistics for the social and behavioral sciences : a practical introduction second edition.

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Modern statistics for the social and behavioral sciences : a practical introduction Second Edition. Edition

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Modern Statistics for the Social and Behavioral Sciences

A Practical Introduction

SECOND EDITION

Modern Statistics for the Social and Behavioral Sciences

A Practical Introduction

SECOND EDITION

University of Southern California

Los Angeles, USA

CRC Press

Taylor & Francis Group

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Boca Raton, FL 33487-2742

© 2017 by Taylor & Francis Group, LLC

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2.3.1RFunctionfortheTrimmedMean

2.3.2AWinsorizedMean

2.3.3RFunctionwinmean

2.4.5WinsorizedVariance

2.4.6RFunctionwinvar

2.4.7MedianAbsoluteDeviation

2.4.8RFunctionmad

2.4.9AverageAbsoluteDistancefromtheMedian

2.4.10

2.5.1AMethodBasedontheMeanandVariance

2.5.2ABetterOutlierDetectionRule:TheMAD-MedianRule

2.5.3RFunctionout

2.5.4TheBoxplot

2.5.5RFunctionboxplot

2.5.6ModificationsoftheBoxplotRuleforDetectingOutliers

2.6.1RFunctionshistandsplot

2.7.1RFunctionskdplotandakerd

3.8.1ComputingthePopulationCovarianceandPearson’sCorrelation

3.9SOMERULESABOUTEXPECTEDVALUES

4.2.1SamplingDistributionoftheSampleMean

4.2.2ComputingProbabilitiesAssociatedwiththeSampleMean

4.3.2ConfidenceIntervalsWhen σ IsNotKnown

4.3.3RFunctionsptandqt

4.3.4ConfidenceIntervalforthePopulationMeanUsingStudent’sT

4.3.5RFunctiont.test

4.4JUDGINGLOCATIONESTIMATORSBASEDONTHEIRSAMPLINGDISTRIBUTION

4.4.1TrimmingandAccuracy:AnotherPerspective

4.7CONFIDENCEINTERVALSFORTHETRIMMEDMEAN

4.7.1EstimatingtheStandardErrorofaTrimmedMean

4.7.2RFunctiontrimse

4.7.3AConfidenceIntervalforthePopulationTrimmedMean

4.7.4RFunctiontrimci

4.9CONFIDENCEINTERVALFORTHEPOPULATIONMEDIAN

4.9.1RFunctionsint

4.9.2EstimatingtheStandardErroroftheSampleMedian

4.9.3RFunctionmsmedse

4.9.4MoreConcernsAboutTiedValues

4.10AREMARKABOUTMOMANDM-ESTIMATORS

4.11CONFIDENCEINTERVALSFORTHEPROBABILITYOFSUCCESS

4.11.1RFunctionsbinomci,acbinomciandandbinomLCO

4.12BAYESIANMETHODS

5.1.1 p-ValueorSignificanceLevel

5.1.2CriticismsofTwo-SidedHypothesisTestingandp-Values

5.1.3SummaryandGeneralization

5.2POWERANDTYPEIIERRORS

5.2.1UnderstandingHown, α,and σ AreRelatedtoPower

5.3TESTINGHYPOTHESESABOUTTHEMEANWHEN

5.3.1RFunctiont.test

5.4CONTROLLINGPOWERANDDETERMININGTHESAMPLESIZE

5.4.1Choosing n PriortoCollectingData

5.4.2RFunctionpower.t.test

5.4.3Stein’sMethod:JudgingtheSampleSizeWhenDataAreAvailable

5.4.4RFunctionsstein1andstein2

5.5PRACTICALPROBLEMSWITHSTUDENT’STTEST

5.6HYPOTHESISTESTINGBASEDONATRIMMEDMEAN

5.6.1RFunctiontrimci

5.6.2RFunctionsstein1.trandstein2.tr

5.7TESTINGHYPOTHESESABOUTTHEPOPULATIONMEDIAN

5.7.1RFunctionsintv2

5.8MAKINGDECISIONSABOUTWHICHMEASUREOFLOCATIONTOUSE

5.9BOOTSTRAPMETHODS

5.10BOOTSTRAP-TMETHOD

5.10.1SymmetricConfidenceIntervals

5.10.2ExactNonparametricConfidenceIntervalsforMeansAreImpossible

5.11THEPERCENTILEBOOTSTRAPMETHOD

5.12INFERENCESABOUTROBUSTMEASURESOFLOCATION

5.12.1UsingthePercentileMethod

5.12.2RFunctionsonesampb,momci,andtrimpb

5.12.3TheBootstrap-tMethodBasedonTrimmedMeans

5.12.4RFunctiontrimcibt

5.13ESTIMATINGPOWERWHENTESTINGHYPOTHESESABOUTATRIMMED

5.13.1RFunctionspowt1estandpowt1an

5.14ABOOTSTRAPESTIMATEOFSTANDARDERRORS

5.14.1RFunctionbootse

5.15EXERCISES

6.2.1ClassicInferentialTechniques

6.2.2MultipleRegression

6.2.3RFunctionsolsandlm

6.3STANDARDIZEDREGRESSION

6.4PRACTICALCONCERNSABOUTLEASTSQUARESREGRESSIONAND HOWTHEYMIGHTBEADDRESSED

6.4.1TheEffectofOutliersonLeastSquaresRegression

6.4.2BewareofBadLeveragePoints

6.4.3BewareofDiscardingOutliersAmongthe Y Values

6.4.4DoNotAssumeHomoscedasticityorthattheRegressionLineis Straight

6.4.5ViolatingAssumptionsWhenTestingHypotheses 211

6.4.6DealingwithHeteroscedasticity:TheHC4Method

6.4.7RFunctionsolshc4andhc4test

6.4.8IntervalEstimationoftheMeanResponse

6.4.9RFunctionolshc4band

6.5PEARSON’SCORRELATIONANDTHECOEFFICIENTOFDETERMINATION

6.5.1ACloserLookatInterpreting r

6.6TESTING H

6.6.1RFunctioncor.test

6.6.2RFunctionpwr.r.test

6.6.3Testing H0: ρ =0WhenThereisHeteroscedasticity

6.6.4RFunctionpcorhc4

6.6.5WhenIsItSafetoConcludethatTwoVariablesAreIndependent?

6.7AREGRESSIONMETHODFORESTIMATINGTHEMEDIANOF Y AND OTHERQUANTILES

6.7.1RFunctionrqfit

6.8DETECTINGHETEROSCEDASTICITY

6.8.1RFunctionkhomreg

6.9INFERENCESABOUTPEARSON’SCORRELATION:DEALINGWITHHETEROSCEDASTICITY

6.9.1RFunctionpcorb

6.10BOOTSTRAPMETHODSFORLEASTSQUARESREGRESSION

6.10.1RFunctionshc4wtest,olswbtest,andlsfitci

6.11DETECTINGASSOCIATIONSEVENWHENTHEREISCURVATURE

6.11.1RFunctionsindtandmedind

6.12QUANTILEREGRESSION

6.12.1RFunctionsqregciandrqtest

6.12.2ATestforHomoscedasticityUsingaQuantileRegressionApproach

6.12.3RFunctionqhomt

6.13 REGRESSION:WHICHPREDICTORSAREBEST?

6.13.3LeastAngleRegression

6.13.4RFunctionlarsR

6.14COMPARINGCORRELATIONS

6.14.1RFunctionsTWOpovandTWOpNOV

6.15CONCLUDINGREMARKS

6.16EXERCISES

7.1.1 ChoosingtheSampleSizes

7.2RELATIVEMERITSOFSTUDENT’ST

7.3WELCH’SHETEROSCEDASTICMETHODFORMEANS

7.3.1Rfunctiont.test

7.3.2Tukey’sThree-DecisionRule

7.3.3NonnormalityandWelch’sMethod

7.3.4ThreeModernInsightsRegardingMethodsforComparingMeans

7.4METHODSFORCOMPARINGMEDIANSANDTRIMMEDMEANS

7.4.1Yuen’sMethodforTrimmedMeans

7.4.2RFunctionsyuenandfac2list

7.4.3ComparingMedians

7.4.4RFunctionmsmed

7.5PERCENTILEBOOTSTRAPMETHODSFORCOMPARINGMEASURESOF LOCATION

7.5.1UsingOtherMeasuresofLocation

7.5.2ComparingMedians

7.5.3RFunctionmedpb2

7.5.4SomeGuidelinesonWhentoUsethePercentileBootstrapMethod

7.5.5RFunctionstrimpb2,med2g,andpb2gen

7.6BOOTSTRAP-TMETHODSFORCOMPARINGMEASURESOFLOCATION

7.6.1ComparingMeans

7.6.2Bootstrap-tMethodWhenComparingTrimmedMeans

7.6.3RFunctionsyuenbtandyhbt

7.6.4EstimatingPowerandJudgingtheSampleSizes

7.6.5RFunctionspowestandpow2an

7.7PERMUTATIONTESTS

7.8RANK-BASEDANDNONPARAMETRICMETHODS

7.8.1Wilcoxon–Mann–WhitneyTest

7.8.2RFunctionswmwandwilcox.test

7.8.3HandlingTiedValuesandHeteroscedasticity

7.8.4Cliff’sMethod

7.8.5RFunctionscidandcidv2

7.8.6TheBrunner–MunzelMethod

7.8.7RFunctionsbmpandloc2dif.ci

7.8.8TheKolmogorov–SmirnovTest

7.8.9RFunctionks

7.8.10ComparingAllQuantilesSimultaneously:AnExtensionofthe Kolmogorov–SmirnovTest

7.8.11RFunctionsband

7.9GRAPHICALMETHODSFORCOMPARINGGROUPS

7.9.1ErrorBars

7.9.2RFunctionsebarplotandebarplot.med

7.9.3PlottingtheShiftFunction

7.9.4PlottingtheDistributions

7.9.5RFunctionsumplot2g

7.9.6OtherApproaches

7.10COMPARINGMEASURESOFVARIATION

7.10.1RFunctioncomvar2

7.10.2Brown–ForsytheMethod

7.10.3ComparingRobustMeasuresofVariation

7.11.1RFunctionsyuenv2andakp.effect

7.12COMPARINGCORRELATIONSANDREGRESSIONSLOPES

7.12.1RFunctionstwopcor,twolsreg,andtworegwb

7.13COMPARINGTWOBINOMIALS

7.13.1Storer–KimMethod

7.13.2Beal’sMethod

7.13.3RFunctionstwobinom,twobici,bi2KMSv2,andpower.prop.test

7.13.4ComparingTwoDiscreteDistributions

7.13.5RFunctiondisc2com

7.14MAKINGDECISIONSABOUTWHICHMETHODTOUSE

7.15EXERCISES

8.1.1 WhenDoesthePairedTTestPerformWell?

8.2COMPARINGROBUSTMEASURESOFLOCATION

8.2.1RFunctionsyuend,ydbt,anddmedpb

8.2.2ComparingMarginalM-Estimators

8.2.3RFunctionrmmest

8.2.4MeasuringEffectSize

8.2.5RFunctionD.akp.effect

8.3HANDLINGMISSINGVALUES

8.4ADIFFERENTPERSPECTIVEWHENUSINGROBUSTMEASURESOFLOCATION

8.4.1RFunctionsloc2difandl2drmci

8.5THESIGNTEST

8.5.1RFunctionsignt

8.6WILCOXONSIGNEDRANKTEST

8.7COMPARINGVARIANCES

8.8.1RFunctionrmrvar

8.9COMPARINGALLQUANTILES

9.1.1 AConceptualOverview

9.1.2ANOVAviaLeastSquaresRegressionandDummyCoding

9.1.3RFunctionsanova,anova1,aov,andfac2list

9.1.4ControllingPowerandChoosingtheSampleSizes

9.2.1Welch’sTest

9.3JUDGINGSAMPLESIZESANDCONTROLLINGPOWERWHENDATAARE

9.3.1RFunctionsbdanova1andbdanova2

9.4TRIMMEDMEANS

9.4.1RFunctionst1way,t1wayv2,t1wayF,andg5plot

9.4.2ComparingGroupsBasedonMedians

9.4.3RFunctionmed1way

9.5BOOTSTRAPMETHODS

9.5.1ABootstrap-tMethod

9.5.2RFunctionst1waybtandBFBANOVA

9.5.3TwoPercentileBootstrapMethods

9.5.4RFunctionsb1way,pbadepth,andQanova

9.6.1AMeasureofEffectSize

9.6.2AHeteroscedasticMethod

9.6.3AMethodBasedonTrimmedMeans

9.6.4RFunctionrananova

9.7RANK-BASEDMETHODS

9.7.1TheKruskall–WallisTest

9.7.2RFunctionkruskal.test

9.7.3MethodBDM

10.2.2InferencesAboutDisordinalInteractions

10.6.2 ThePatel–HoelApproachtoInteractions

11.1COMPARINGMEANSINAONE-WAYDESIGN

11.1.1 RFunctionaov

11.2COMPARINGTRIMMEDMEANSWHENDEALINGWITHAONE-WAYDESIGN

11.2.1RFunctionsrmanovaandrmdat2mat

11.2.2ABootstrap-tMethodforTrimmedMeans

11.3PERCENTILEBOOTSTRAPMETHODSFORAONE-WAYDESIGN

11.3.1MethodBasedonMarginalMeasuresofLocation

11.3.3InferencesBasedonDifferenceScores

11.6.2RFunctionbwtrimandbw2list

11.6.4RFunctiontsplitbt

11.6.5InferencesBasedonM-estimatorsandOtherRobustMeasuresof Location

11.8.1RFunctionsbbwtrim,bwwtrim,andwwwtrim

11.8.2DataManagement:RFunctionsbw2listandbbw2list

12.1ONE-WAYANOVAANDRELATEDSITUATIONS,INDEPENDENTGROUPS

12.1.1Fisher’s LeastSignificantDifferenceMethod

12.1.2TheTukey–KramerMethod

12.1.3RFunctionTukeyHSD

12.1.4Tukey–KramerandtheANOVAFTest

12.1.5Step-DownMethods

12.1.6Dunnett’sT3

12.1.7Games–HowellMethod

12.1.8ComparingTrimmedMeans

12.1.9RFunctionslincon,stepmcpandtwoKlin

12.1.10AlternativeMethodsforControllingFWE

12.1.11PercentileBootstrapMethodsforComparingTrimmedMeans,Medians,andM-estimators 468

12.1.12RFunctionsmedpb,linconpb,pbmcp,andp.adjust

12.1.13ABootstrap-tMethod

12.1.14RFunctionlinconbt

12.1.15Rank-BasedMethods

12.1.16RFunctionscidmul,cidmulv2,andbmpmul

12.1.17ComparingtheIndividualProbabilitiesofTwoDiscreteDistributions

12.1.18RFunctionsbinband,splotg2,cumrelf,andcumrelfT

12.1.19ComparingtheQuantlilesofTwoIndependentGroups

12.1.20RFunctionsqcomhdandqcomhdMC

12.1.21MultipleComparisonsforBinomialandCategoricalData

12.1.22RFunctionsskmcpanddiscmcp

12.2TWO-WAY,BETWEEN-BY-BETWEENDESIGN

12.2.1Scheff´e’sHomoscedasticMethod

12.2.2HeteroscedasticMethods

12.2.3ExtensionofWelch–˘ Sid´akandKaiser–BowdenMethodstoTrimmed Means 481

12.2.4RFunctionkbcon

12.2.5RFunctionscon2wayandconCON 482

12.2.6LinearContrastsBasedonMedians

12.2.7RFunctionsmsmedandmcp2med

12.2.8BootstrapMethods

12.2.9RFunctionsmcp2a,bbmcppb,bbmcp 487

12.2.10ThePatel–HoelRank-BasedInteractionMethod 487

12.2.11RFunctionrimul 487

12.3JUDGINGSAMPLESIZES

12.3.1 Tamhane’sProcedure

12.3.2RFunctiontamhane

12.3.3Hochberg’sProcedure

12.3.4RFunctionhochberg

12.4METHODSFORDEPENDENTGROUPS

12.4.1LinearContrastsBasedonTrimmedMeans

12.4.2RFunctionrmmcp

12.4.3ComparingM-estimators

12.4.4RFunctionsrmmcppb,dmedpb,dtrimpb,andboxdif

12.4.5Bootstrap-tMethod

12.4.6RFunctionbptd

12.4.7ComparingtheQuantilesoftheMarginalDistributions

12.4.8RFunctionDqcomhd

12.5BETWEEN-BY-WITHINDESIGNS

12.5.1RFunctionsbwmcp,bwamcp,bwbmcp,bwimcp,spmcpa,spmcpb, spmcpi,andbwmcppb 497

12.6WITHIN-BY-WITHINDESIGNS

12.6.1Three-WayDesigns

12.6.2RFunctionscon3way,mcp3atm,andrm3mcp

12.6.3BootstrapMethodsforThree-WayDesigns

12.6.4RFunctionsbbwmcp,bwwmcp,bwwmcppb,bbbmcppb,bbwmcppb, bwwmcppb,andwwwmcppb 502

13.1.1 DetectingOutliersViaRobustMeasuresofLocationandScatter

13.1.2RFunctionscov.mveandcov.mcd

13.1.3MoreMeasuresofLocationandCovariance

13.1.4RFunctionsrmba,tbs,andogk

13.1.5RFunctionout 514

13.1.6AProjection-TypeOutlierDetectionMethod

13.1.7RFunctionsoutpro,outproMC,outproad,outproadMC,andout3d 516

13.1.8SkippedEstimatorsofLocation 517

13.1.9RFunctionsmean 517

13.2ONE-SAMPLEHYPOTHESISTESTING

13.2.1ComparingDependentGroups

13.2.2RFunctionssmeancrv2,hotel1,andrmdzeroOP

13.3TWO-SAMPLECASE 523

13.3.1RFunctionssmean2,mat2grp,matsplit,andmat2list

13.3.2Rfunctionsmatsplit,mat2grp,andmat2list

13.4.1RFunctionmanova

13.4.2RobustMANOVABasedonTrimmedMeans

13.4.3RFunctionsMULtr.anovaandMULAOVp

13.5AMULTIVARIATEEXTENSIONOFTHEWILCOXON–MANN–WHITNEY TEST

13.5.1ExplanatoryMeasureofEffectSize:AProjection-TypeGeneralization

13.6.1TheMunzel–BrunnerMethod

13.6.2RFunctionmulrank

13.6.3TheChoi–MardenMultivariateRankTest 534

13.6.4RFunctioncmanova

13.7MULTIVARIATEREGRESSION

13.7.1MultivariateRegressionUsingR

13.7.2RobustMultivariateRegression

13.8.1RFunctionsprcompandregpca

13.8.2RobustPrincipalComponents

13.8.3RFunctionsoutpca,robpca,robpcaS,Ppca,andPpca.summary 544

14.1.1 TheTheil–SenEstimator

14.1.2RFunctionstsreg,tshdreg,andregplot

14.1.3LeastMedianofSquares 553

14.1.4LeastTrimmedSquaresandLeastTrimmedAbsoluteValueEstimators 553

14.1.5RFunctionslmsreg,ltsreg,andltareg

14.1.6M-estimators

14.1.7RFunctionchreg

14.1.8DeepestRegressionLine 555

14.1.9RFunctionmdepreg 555

14.1.10SkippedEstimators 555

14.1.11RFunctionsopregandopregMC 555

14.1.12S-estimatorsandanE-typeEstimator 555

14.1.13RFunctiontstsreg 556

14.2 COMMENTSONCHOOSINGAREGRESSIONESTIMATOR

14.3INFERENCESBASEDONROBUSTREGRESSIONESTIMATORS

14.3.1TestingHypothesesAbouttheSlopes

14.3.2InferencesAbouttheTypicalValueof Y Given X

14.3.3RFunctionsregtest,regtestMC,regci,regciMC,regYci,andregYband 559

14.3.4ComparingMeasuresofLocationviaDummyCoding

14.4DEALINGWITHCURVATURE:SMOOTHERS

14.4.1Cleveland’sSmoother

14.4.2RFunctionslowess,lplot,lplot.pred,andlplotCI

14.4.3SmoothersBasedonRobustMeasuresofLocation 566

14.4.4RFunctionsrplot,rplotCIS,rplotCI,rplotCIv2,rplotCIM,rplot.pred, qhdsm,andqhdsm.pred 568

14.4.5PredictionWhen X IsDiscrete:TheRFunctionrundis

14.4.6SeeingCurvaturewithMoreThanTwoPredictors

14.4.7RFunctionprplot

14.4.8SomeAlternativeMethods

14.4.9DetectingHeteroscedasticityUsingaSmoother

14.4.10RFunctionrhom

14.5SOMEROBUSTCORRELATIONSANDTESTSOFINDEPENDENCE

14.5.1Kendall’stau

14.5.2Spearman’srho

14.5.3WinsorizedCorrelation

14.5.4RFunctionwincor

14.5.5OPorSkippedCorrelation

14.5.6RFunctionscor

14.5.7InferencesaboutRobustCorrelations:DealingwithHeteroscedasticity

14.5.8RFunctionscorbandscorci

14.6MEASURINGTHESTRENGTHOFANASSOCIATIONBASEDONAROBUSTFIT 577

14.7COMPARINGTHESLOPESOFTWOINDEPENDENTGROUPS

14.7.1RFunctionreg2ci

14.8TESTSFORLINEARITY

14.8.1RFunctionslintest,lintestMC,andlinchk

14.9IDENTIFYINGTHEBESTPREDICTORS

14.9.1InferencesBasedonIndependentVariablesTakeninIsolation

14.9.2RFunctionsregpord,ts2str,andsm2strv7

14.9.3InferencesWhenIndependentVariablesAreTakenTogether 585

14.9.4RFunctionregIVcom

14.10INTERACTIONSANDMODERATORANALYSES

14.10.1 RFunctionsolshc4.inter,ols.plot.inter,regci.inter,reg.plot.interand adtest 590

14.10.2GraphicalMethodsforAssessingInteractions 591

14.10.3RFunctionskercon,runsm2g,regi 592 14.11ANCOVA 593

14.11.1ClassicANCOVA 594

14.11.2RobustANCOVAMethodsBasedonaParametricRegressionModel 596

14.11.3RFunctionsancJN,ancJNmp,anclin,reg2plot,andreg2g.p2plot 597

14.11.4ANCOVABasedontheRunning-intervalSmoother 599

14.11.5RFunctionsancsm,Qancsm,ancova,ancovaWMW,ancpb,ancovaUB,ancboot,ancdet,runmean2g,qhdsm2g,andl2plot 600

14.11.6RFunctionsDancts,Dancols,Dancova,Dancovapb,DancovaUB, andDancdet 604

15.1GOODNESSOFFIT

15.1.1R Functionschisq.testandpwr.chisq.test

15.2.1RFunctionchi.test.ind

15.3DETECTINGDIFFERENCESINTHEMARGINALPROBABILITIES

15.3.1RFunctionscontabandmcnemar.test

15.4.1TheProportionofAgreement

15.4.2Kappa

15.4.3WeightedKappa

15.4.4RFunctionCkappa

15.5LOGISTICREGRESSION 623

15.5.1RFunctionsglmandlogreg

15.5.2AConfidenceIntervalfortheOddsRatio

15.5.3RFunctionODDSR.CI

15.5.4SmoothersforLogisticRegression 626

15.5.5RFunctionslogrsm,rplot.bin,andlogSM 627

15.6EXERCISES 627

PREFACEtotheSECOND EDITION

Thissecondeditioncontainsmajorchangesandadditions.Allofthechaptershavebeen updated;forsomethereareonlyminorchanges,butforotherstherearemajorchanges. ManynewRfunctionshavebeenaddedthatreflectrecentadvances.Briefly,theydealwith newmethodsforcomparinggroupsandstudyingassociations.Forexample,itisnowpossible tocompareacollectionofquantilesinamannerthatcontrolstheprobabilityofoneormore TypeIerrors,evenwhentherearetiedvalues.Thisnewapproachcanprovideadeeper understandingofhowgroupscompareincontrasttomethodsthatfocusonasinglemeasure oflocation.Somenewmultiplecomparisonproceduresarecoveredaswellassomenew techniquesrelatedtotestingglobalhypotheses.Newmethodsforcomparingregressionlines arecoveredandseveralnewandimprovedmethodsfordealingwithANCOVAaredescribed. Thereisevenanewmethodfordeterminingwhichindependentvariablesaremostimportant inaregressionmodel.Therearemanywaysofestimatingwhichvariablesaremostimportant. Whatisnewhereistheabilitytodeterminethestrengthoftheempiricalevidencethatthe mostimportantvariablesarecorrectlyidentified.

Detailsaboutbasicbootstrapmethodsnowappearwhensamplingdistributionsareintroduced.Thisisincontrasttothefirsteditionwherebootstrapmethodswereintroducedina separatechapter.Introducingbootstrapmethodsinconjunctionwithsamplingdistributions seemstohelpsomestudentsunderstandsamplingdistributions.

Asolutionmanual,whichcontainsadetailedsolutiontoalloftheexercises,isnow availableontheauthor’swebsite:

Dornsife.usc.edu/cf/labs/wilcox/wilcox-faculty-display.cfm. Perhapsaneasierwayofreachingtheauthor’swebpageistosearchforSoftwareRand Wilcox.Clickingonthislinkwilltakeyoutotheauthor’swebpage.Gotothesection labeledbooks.ThefileCRC answers.pdfthecontainssolutionmanual.Mostofthedata used inthisbookareavailableontheauthor’swebpageandarestoredinthesectionlabeled datasets.

PREFACE

Therearetwogeneralgoalsinthisbook.Assumingthereaderhashadnopriortraining in statistics,thefirstistoprovideagraduate-levelintroductiontobasic,routinelyused statisticaltechniquesrelevanttothesocialandbehavioralsciences.Ascanbeseenfromthe tableofcontents,awiderangeoftopicsiscovered.Includedisanontechnicaldescription ofthreemajorinsightsregardingwhenroutinelyusedmethodsperformwellandwhenthey arehighlyunsatisfactory.Oneoftheseinsightshastodowithsituationswheredataare sampledfromwhatiscalledaheavy-taileddistribution.Suchdistributionsareroutinely encountered.Duringthelasthalfcentury,hundredsofpublishedpapersdescribepractical concernsandhowtheymightbeaddressed.Thesecondgeneralinsighthastodowiththe effectsofskewness.Adeeperunderstandingofhowtodealwithskeweddistributionsand whyskeweddistributionscanwreakhavoconanyinferentialmethodbasedinpartonaleast squaresestimator,havebeendeveloped.Thethirdinsighthastowithheteroscedasticity.In practicalterms,routinelytaughtandusedmethodscanresultinrelativelylowpowerand highlymisleadingresults,contrarytowhatwasoncebelieved.Explanationsareprovided aboutwhyitwasoncethoughtthatclassicmethodsarerobusttoviolationsofassumptions andwhyitisnowknownthatundergeneralconditions,thisisnotthecase.

Thesecondgeneralgoalistodescribeandillustratemethodsdevelopedduringthelast halfcenturythatdealwithknownproblemsassociatedwithclassictechniques.Thereisincreasingawarenessamongnonstatisticiansthatthesenewermethodscanmakeaconsiderable differenceintheconclusionsreachedwhenanalyzingdata.Andnumerousillustrationsare providedthatsupportthisview.Animportantpointisthatnosinglemethodisalwaysbest. Sowhatisneededisageneralunderstandingoftherelativemeritsofvarioustechniquesso thatthechoiceofmethodcanbemadeinaninformedmanner.

Asforsoftware,thisbookfocusesonR,whichisintroducedin Chapter1.Interms oftakingadvantageofmodernstatisticaltechniques,Rclearlydominates.Whenanalyzing data,itisundoubtedlythemostimportantsoftwaredevelopmentduringthelastquarterofa century.Anditisfree.Althoughclassicmethodshavefundamentalflaws,itisnotsuggested thattheybecompletelyabandoned.Anditisimportanttoknowhowtoapplyandinterpret thesemethodssimplybecausetheyarestillroutinelyused.Consequently,illustrationsare providedonhowtoapplystandardmethodswithR.Ofparticularimportancehereisthat avastarrayofmodernmethodscanbeappliedviaanRpackagethatcontainsover1300R functions,manyofwhichareillustratedinthisbook.

INTRODUCTION

Statisticalmethodsthatareusedbyawiderangeofdisciplinesconsistofatleastthreebasic components:

• Experimentaldesign,meaningtheplanningandcarryingoutofastudy.

• Summarizingdata,usingwhatarecalleddescriptivestatistics.

• Inferentialtechniques,whichroughlyaremethodsaimedatmakingpredictionsorgeneralizationsaboutapopulationofindividualsorthingswhennotallindividualsor thingscanbemeasured.

Thefundamentalgoalinthisbookistosummarizethebasicstatisticaltechniquesassociated withthesethreecomponents,withanemphasisonthelattertwocomponents,inamanner thatmakesthemaccessibletostudentsnotmajoringinstatistics.Ofparticularimportance isfosteringtheabilityofthereadertothinkcriticallyabouthowdataaresummarizedand analyzed.

Themathematicalfoundationofthestatisticaltoolsroutinelyusedtodaywasdeveloped abouttwocenturiesagobyPierre-SimonLaplaceandCarlFriedrichGaussinaseriesof remarkableadvances.Aboutacenturyago,importantrefinementsandextensionsweremade byKarlPearson,JerzyNeyman,EgonPearson,WilliamGosset,andSirRonaldFisher.The strategiesandmethodsthattheydevelopedareroutinelyusedtoday.

Duringthelasthalfcentury,however,literallyhundredsofjournalarticleshavemade itabundantlyclearthattherearethreebasicconcernsassociatedwiththeseroutinelyused techniquesthatareoffundamentalimportance.Thisisnottosaythattheyshouldbe abandoned,butitisimportanttounderstandtheirlimitationsaswellashowtheselimitations mightbeaddressedwithmethodsdevelopedduringthelasthalfcentury.Itisevidentthat anyroutinelyusedstatisticalmethodthataddressesbasicissuesneedstobecoveredin anyintroductorystatisticsbookaimedatstudentsandresearcherstryingtounderstand theirdata.Simultaneously,itseemsequallyevidentthatwhenrelevantinsightsaremade regardingtheproperuseandinterpretationofthesemethods,theyshouldbeincludedin anintroductorybookaswell.Omissionofsomemoderninsightsmightbeacceptableif theresultswereatsomelevelcontroversialamongstatisticiansfamiliarwiththeunderlying

principles.Butwhentherearehundredsofpapersacknowledgingaproblemwitharoutinely usedmethod,withnocounterargumentsbeingofferedinareputablestatisticsjournal,surely itisimportanttodiscussthepracticalimplicationsoftheinsightinabookaimedatnonstatisticians.Thisisthepointofviewadoptedhere.

1.1SAMPLESVERSUSPOPULATIONS

Assumingthereaderhasnopriortraininginstatistics,webeginbymakingadistinction betweenapopulationofindividualsofinterestandasampleofindividuals.A population ofparticipantsorobjectsconsistsofallthoseparticipantsorobjectsthatarerelevantina particularstudy.

Definition:A sample isanysubsetofthepopulationofindividualsorthingsunder study.

EXAMPLE

Imagineastudydealingwiththequalityofeducationamonghigh-schoolstudents.One aspectofthisissuemightbethenumberofhoursstudentsspendonhomework.Imaginethat 100studentsareinterviewedataparticularschooland40saytheyspendlessthan1hour onhomework.The100studentsrepresentasample;theyareasubsetofthepopulationof interest,whichisallhigh-schoolstudents.

EXAMPLE

Imagineadevelopmentalpsychologiststudyingthewayschildreninteract.Oneaspect ofinterestmightbethedifferencebetweenmalesandfemalesintermsofhowtheyhandle certainsituations.Forexample,areboysmoreaggressivethangirlsincertainplaysituations? Imaginethatthepsychologistvideotapes4-year-oldchildrenplaying,andthenratersrate eachchildona10-pointscaleintermsoftheamountofaggressivebehaviortheydisplay. Furtherimaginethat30boysgetanaverageratingof5,while25girlsgetanaveragerating of4.The30boysrepresentasamplefromtheentirepopulationof4-year-oldboysandthe 25girlsrepresentasamplefromthepopulationofall4-year-oldgirls.

Inferentialmethodsarebroadlyaimedatassessingtheimplicationsofasampleregarding thecharacteristicsofapopulation.Inthelastexample,the30boyshaveahigheraverage ratingthanthe25girls.Basedonthisresult,isitreasonabletoconcludethatifall4-year-old boyscouldbemeasured,aswellasall4-year-oldgirls,itwouldagainbethecasethatthe averageratingforboyswouldbehigherthantheaverageratingforgirls?Thisisoneofthe manyissuesaddressedinthisbook.

Noticethatinthislastexample,anaveragewasusedwiththegoalofcharacterizingthe typicalboyandgirl.Arethereotherwaysofsummarizingthedatathathavepracticalimportance?Theanswerisanunequivocalyes,aswillbemadeevidentinsubsequentchapters. Indeed,oneofthemajoradvancesduringthelasthalfcenturyisabetterunderstandingof therelativemeritsassociatedwithvariouswaysofsummarizingdata.

1.2SOFTWARE

Thereareanumberofsoftwarepackagesaimedatprovidingeasyaccesstoroutinelyused statisticaltechniques.Thefocusinthisbookisonthefree(opensource)softwarepackage R,whichwithinstatisticsisarguablythemostimportantsoftwaredevelopmentduringthe

lastquarterofa century.Itispowerful,flexible,anditprovidesarelativelysimpleway ofapplyingcutting-edgetechniques.Rpackagesareroutinelywrittenbystatisticiansfor applyingrecentlydevelopedmethods,whichareeasilyinstalledbyanyoneusingR.This isextremelyimportantbecauserelyingoncommercialsoftwarehasproventobehighly unsatisfactorywhenitcomestoapplyingthemanyimportantmethodsdevelopedduringthe lasthalfcentury.Rcanbeinstalledbygoingtothewebsite

www.R-project.org

S-plusisverysimilartoRwithoneimportantdifference:itcanbeexpensive.Allofthe methodsinthisbookcanbeappliedwithR,butthisisnotthecasewhenusingS-plus.

SPSSispopularamongacademics,butgainingaccesstomodernmethodsisdifficult andtypicallyimpossible.Generally,usingSPSSrequiresignoringthethreemajorproblems associatedwithclassicmethodsthataredescribedinthisbook.Moreover,comparedtoR, SPSSishighlyinflexiblewhenitcomestoapplyingthemanynewandimprovedmethods thathaveappearedduringthelasthalfcentury.BecauseSPSSdoesnotupdateitssoftware inanadequatemanner,itwasexcludedfromconsiderationwhenchoosingsoftwareforthis book.

SASisanotherwell-knownsoftwarepackagethatoffershighpowerandafairdegreeof flexibility.SASsoftwarecouldbewrittenforapplyingthemodernmethodsmentionedinthis book,butformanyofthetechniquestobedescribed,thishasnotbeendone.

Anotherwell-knownsoftwarepackageisEXCEL.Itwasexcludedfromconsideration basedonreviewsbyMcCulloughandWilson(2005)aswellasHeiser(2006),whichconclude thatthispackageisnotmaintainedinanadequatemanner.Indeed,evencertainbasicfeatures havefundamentalflaws.

1.3RBASICS

Aspreviouslynoted,Rcanbedownloadedfrom www.R-project.org.Afreeandveryuseful interfaceforR isRStudio(www.rstudio.com).Many booksareavailablethatarefocusedon thebasicsofR(e.g.,Crawley,2007;VenablesandSmith,2002;Verzani,2004;Zuuretal., 2009).ThebookbyVerzani(2004)isavailableonthewebat

http://cran.r-project.org/doc/contrib/Verzani-SimpleR.pdf

Rcomeswithavastarrayofmethodsforhandlingandanalyzingdata.Explainingallof itsfeaturesandbuilt-infunctionsisimpossiblehereandtoalargeextentnotnecessaryfor presentpurposes.Here,thefocusisonthemorebasicfeaturesneededtoapplystandard statisticalmethods,aswellasthemoremodernmethodscoveredinthisbook. OnceyoustartRyouwillseethisprompt: > Thispromptisnottyped,itmerelymeansthatRiswaitingforacommand.ToquitR,use thecommand > q()

1.3.1EnteringData

Tobeginwiththesimplestcase,imagineyouwanttostorethevalue5inanRvariablecalled dat.Thiscanbedonewiththecommand

Typing dat andhittingEnterwillproducethevalue5onthecomputerscreen. Tostorethevalues2,4,6,8,12intheRvariable dat,usetheccommandwhichstands for“combine.”Thatis,thecommand

dat=c(2,4,6,8,12))

willstorethesevaluesintheRvariable dat

ToreaddatastoredinafileintoanRvariable,usethe scan commandorthe read.table command.BothofthesecommandsassumethedataarestoredinthedirectorywhereR expectstofindthefile.Asimplewayoffindingaparticularfileiswiththethefile.choose, whichwillbeillustrated.The scan commandisconvenientwhenworkingwithasingle variable; read.table isconvenientwhenworkingwithtwoormorevariables.Inthesimplest case,itisassumedthatvaluesareseparatedbyoneormorespaces. Missingvalues are assumedtoberecordedasNAfor“notavailable.”Forexample,imaginethatafilecalled ice.dat contains 631289

Thenthecommand

dat=scan(file=”ice.dat”)

willreadthesevaluesfromthefileandstorethemintheRvariable dat havingwhatis calledavectormode.Roughly,avectorisjustacollectionofvaluesassociatedwithasingle variable.Whenusingthescancommand,thefilenamemustbeinquotes. Ifinsteadafilecalled dis.data thatcontains

1264 7NA8 1182

thenthecommand

dat2=scan(file=”dis.data”)

willstorethedataintheRvariabledat2.Typingdat2andhittingEnterreturns 12647NA81182

WhenyouquitRwiththecommand q(),Rwillaskwhetheryouwanttosavethe workspace.Ifyouenter y,valuesstoredinRvariablesstaythereuntiltheyareremoved.So inthislastexample,ifyouturnoffyourcomputer,andthenturnitbackon,typing dat2 will againreturnthevaluesjustdisplayed.Toremovedata,usethe rm command.Forexample, rm(dat)

wouldremovetheRvariable dat Rvariablesarecasesensitive.So,forexample,thecommand

Dat2=5

wouldstorethevalue5inDat2,buttheRvariabledat2wouldstillcontaintheninevalues listedpreviously,unless,ofcourse,theyhadbeenremoved.

TheRcommand read.table isanothercommonlyusedmethodforreadingdataintoan R.Itisconvenientwhendealingwithtwoormorevariables,withthevaluesforeachvariable storedincolumns.Ithasthegeneralform

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MP24828.

Stalking the wild cranberry: the making of a T V commercial. George Gage Productions. Produced in cooperation with the Beverage and Breakfast Foods Division of General Foods Corporation, Benton and Bowles, Inc. & Euell Gibbons. 22 min., sd., color, 16 mm. © American Association of Advertising Agencies; 17Oct73; MP24828.

MP24829.

The Baggs. A Solari Carr production. 12 min., sd., color, 16 mm. Appl. au.: Tom Solari, Clark Carr & James F. Griffith. © Tom Solari & Clark Carr; 26Oct73; MP24829.

MP24830.

We’ve come of age. National Council of Senior Citizens. Made by Scad Promotions, Inc. 12 min., sd., color, 16 mm. Appl. au.: Stan Anton. © National Council of Senior Citizens; 1Nov73; MP24830.

MP24831.

Wind raiders of the Sahara. National Geographic Society & Wolper Productions. 50 min., sd., color, 16 mm. © National Geographic Society; 28Aug73; MP24831.

MP24832.

Newspaper story. Encyclopaedia Britannica Educational Corporation. 2nd edition. 27 min., sd., color, 16 mm. (The World of work) Prev. pub. 10Apr50, M42. © Encyclopaedia Britannica Educational Corporation; 19Jul73; MP24832.

MP24833.

T V news: behind the scenes. Encyclopaedia Britannica Educational Corporation. 27 min., sd., color, 16 mm. (The World of work) © Encyclopaedia Britannica Educational Corporation; 17Aug73; MP24833.

MP24834.

The Mayfly: ecology of an aquatic insect. Encyclopaedia Britannica Educational Corporation. 15 min., sd., color, 16 mm. © Encyclopaedia Britannica Educational Corporation; 6Jul73; MP24834.

MP24835.

Starting school. Encyclopaedia Britannica Educational Corporation. 14 min., sd., color, 16 mm. © Encyclopaedia Britannica Educational Corporation; 20Jul73; MP24835.

MP24836.

The American Indian speaks. Encyclopaedia Britannica Educational Corporation. 23 min., sd., color, 16 mm. © Encyclopaedia Britannica Educational Corporation; 27Jul73; MP24836.

MP24837.

Diffusion and osmosis. Encyclopaedia Britannica Educational Corporation. 14 min., sd., color, 16 mm. © Encyclopaedia Britannica Educational Corporation; 24Aug73; MP24837.

MP24838.

Volcanoes: exploring the restless earth. Encyclopaedia Britannica Educational Corporation. Produced in cooperation with the American Geological Institute. 18 min., sd., color, 16 mm. © Encyclopaedia Britannica Educational Corporation; 21Sep73; MP24838.

MP24839.

What do you do while you wait? Maclovia. 11 min., sd., color, 16 mm. © Encyclopaedia Britannica Educational Corporation; 18Jul73; MP24839.

MP24840.

The Farmer in a changing America. Encyclopaedia Britannica Educational Corporation. 27 min., sd., color, 16 mm. (The Rise of industrial America) © Encyclopaedia Britannica Educational Corporation; 6Jul73; MP24840.

MP24841.

The United States Supreme Court: guardian of the Constitution. Encyclopaedia Britannica Educational Corporation. Made by Concept Films. 2nd edition. 24 min., sd., color, 16 mm. Prev. pub. 9Dec54, 5848. © Encyclopaedia Britannica Educational Corporation; 11Jun73; MP24841.

MP24842.

Say it — moving. 30 sec., sd., color, 16 mm. Appl. au.: William Esty Company, Inc. © Colgate Palmolive Company; 12Nov73; MP24842.

MP24843.

Gloriously clean/campus. 30 sec., sd., color, 16 mm. Appl. au.: William Esty Company, Inc. © Colgate Palmolive Company; 12Nov73; MP24843.

MP24844.

The Least of these, my brethren. A John R. Gregory production. Produced in cooperation with the University of Southern Missions. 14 min., sd., color, 16 mm. © John R. Gregory; 10Apr72; MP24844.

MP24845.

Stabilization: holding the roads. Engineering Research Institute, Soil Research Laboratory & the Film Production Unit, Iowa State University. 21 min., sd., color, 16 mm. © Iowa State University a. a. d. o. Iowa State University of Science and Technology; 20Oct73; MP24845.

MP24846.

Walking with the Master. 22 min., si., color, 16 mm. Appl. au.: Mani S. Irani. © Mani S. Irani; 23Jun73; MP24846.

MP24847.

First aid action. The Sidaris Company, a subsidiary of Penn-Pacific Corporation. Produced in cooperation with the American National Red Cross, Los Angeles Chapter. 20 min., sd., color, 16 mm. © The Sidaris Company; 12Nov73; MP24847.

MP24848.

Sexuality in the medical school curriculum. Ortho Pharmaceutical Corporation. 3 motion pictures (29 min., 35 min., 31 min.), sd., color, 16 mm. © Ortho Pharmaceutical Corporation; 21May73 (in notice: 1972); MP24848.

MP24849.

2 guys on 1 girl. 12 min., si., b & w, 8 mm. © Diverse Industries, Inc.; 15Nov73; MP24849.

MP24850.

Teenage pajama party. 12 min., si., b & w, 8 mm. © Diverse Industries, Inc.; 15Nov73; MP24850.

MP24851.

Candy and Dick. 12 min., si., b & w, 8 mm. © Diverse Industries, Inc.; 15Nov73; MP24851.

MP24852.

Tits galore. 12 min., si., b & w, 8 mm. © Diverse Industries, Inc.; 15Nov73; MP24852.

MP24853.

Henry Miller, asleep and awake. Tom Schiller. 35 min., sd., color, 16 mm. © Thomas Bennett Schiller d. b. a. Tom Schiller Films; 5Dec73; MP24853.

MP24854.

Miller on special problems in the older diabetic. Science and Medicine Films, a division of Science and Medicine Publishing Company, Inc. 6 min., sd., color. 16 mm. (Upjohn Mini-text series) Prev. reg. 18Jun73. NM: Mini-text prologue. © The Upjohn Company; 8Jan74 (in notice: 1973); MP24854.

MP24855.

Swans of Red Rock Lakes. Pt. 2. A Don Meier production. 23 min., sd., color, 16 mm. (Mutual of Omaha’s Wild kingdom) Add. ti.: Swans of Red Rocks Lake. NM: cinematographic work. © Mutual of Omaha; 25Jan74; MP24855.

MP24856.

Swans of Red Rock Lakes. Pt. 1. A Don Meier production. 23 min., sd., color, 16 mm. (Mutual of Omaha’s Wild kingdom) Add. ti.:

Swans of Red Rocks Lake. NM: cinematographic work. © Mutual of Omaha; 25Jan74; MP24856.

MP24857.

Adventure above the Arctic Circle. A Don Meier production. 23 min., sd., color, 16 mm. (Mutual of Omaha’s Wild kingdom) NM: cinematographic work. © Mutual of Omaha; 31Nov73; MP24857.

MP24858.

Coyote country. A Don Meier production. 23 min., sd., color, 16 mm. (Mutual of Omaha’s Wild kingdom) NM: cinematographic work. © Mutual of Omaha; 28Dec73; MP24858.

MP24859.

Isles of enchantment. Pt. 2. A Don Meier production. 23 min., sd., color, 16 mm. (Mutual of Omaha’s Wild kingdom) NM: cinematographic work. © Mutual of Omaha: 2Nov73; MP24859.

MP24860.

Bass, U. S. A. A Mort Neff-Francis Carter Wood production. 25 min., sd., color, 16 mm. © Francis Carter Wood, Inc.; 15Dec73; MP24860.

MP24861.

Digital computer engineering laboratory. 15 min., sd., color, 16 mm. Appl. au.: The University of Michigan, Audio Visual Center. © The University of Michigan; 1Dec73; MP24861.

MP24862.

The Energy environment game. A Portafilms production. 22 min., sd., color, 16 mm. © Edison Electric Institute; 2Jul73; MP24862.

MP24863.

About candy. 11 min., sd., color, 16 mm. (About) NM: abridgment. © Films, Inc.; 20Mar73 (in notice: 1972); MP24863.

MP24864.

About astronauts. 10 min., sd., color, 16 mm. (About) Appl. states copyright not claimed in official NASA films. NM: abridgment. © Films, Inc.: 20Mar73 (in notice: 1972); MP24864.

MP24865.

About horses. 10 min., sd., color, 16 mm. (About) Prev. pub. 1967. NM: abridgment. © Films, Inc.; 2Mar73 (in notice: 1972); MP24865.

MP24866.

About dogs. 11 min., sd., color, 16 mm. (About) Prev. pub. 1966. NM: abridgment. © Films, Inc.; 9Jan73 (in notice: 1972); MP24866.

MP24867.

About cats. 11 min., sd., color, 16 mm. (About) Prev. pub. 1968. NM: abridgment. © Films, Inc.; 29Nov72; MP24867.

MP24868.

Two brothers in Greece. 17 min., sd., color, 16 mm. (Man and his world) NM: abridgment. © Public Media, Inc.; 25Sep69; MP24868.

MP24869.

Venezuela. 12 min., sd., color, 16 mm. (Man and his world) NM: abridgment. © Public Media, Inc.; 23Apr70; MP24869.

MP24870.

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