data analysis and ethics數(shù)據(jù)分析與倫理

Presenter:ZHANGJinLIYue2010.10.25DataAnalysisandEthicsinBusinessResearchOutline1.PrinciplesandProceduresofExploratoryDataAnalysis2.MissingData3.LongitudinalandCross-sectionalTests4.DataAnalyticTrendsandDoctoralTraining5.EthicalIssuesinBusinessResearch6.FromtheEditors:anEthicalQuiz7.AcademyofManagementCodeofEthicalConduct1.1PrinciplesofExploratoryDataAnalysisWhatisEDA?AdetectiveworktodiscoverpatternsindatawithdifferentmethodsAnattitudetoexploredataconsistentlyandthoroughlyAplausiblestorytotellratherthandrawingconclusionsDefinitionExploratorydataanalysis(EDA)isawell-establishedstatisticaltraditionthatprovidesconceptualandcomputationaltoolsfordiscoveringpatternstofosterhypothesisdevelopmentandrefinement.EDA&CDAFormulationvs.Test(Generatingthedirectionvs.Testingthemyth)EDAhelpstointerpretresultsofCDAandmayrevealunexpectedormisunderstandingofpatternsinthedata.1.2ProceduresofEDABelief:GetarichdescriptionofdataUseofgraphics;Processofiterativemodelfit;ResidualAnalysisUnderstandtheContextInteractionofpriorknowledgeandpresentdataanalysisQuantitativeknowingdependsonqualitativeknowingFundamentalobservationswithstatisticalabilityUseGraphicRepresentationofData

“GraphicanalysisiscentraltoEDA”PortraynumerousdatavaluessimultaneouslySimpleplot;stem-and-leafplot;dotplot;boxplot;densitysmoothers;interactivecomputergraphics.1.2ProceduresofEDA——GraphicsGraphicRepresentationofData(1):SimplePlotLinearregressionStraightandsimpletounderstand1.2ProceduresofEDA——GraphicsGraphicRepresentationofData(2):BoxPlotMarkfirstandthirdquartilesOfferinformationaboutthelocationofkeyelementsinthedistributionandomitmoresubtledetailsUsefulwhenanumberofdistributionneedtobecompared1.2ProceduresofEDA——GraphicsGraphicRepresentationofData(3):DensitySmoothersDisclosesomehiddeninformationOverlayingdensityfunctionallowsdirectcomparisonofshape1.2ProceduresofEDADevelopModelsinanIterativeProcessandTentativeModelSpecificationandResidualAssessmentData=Fit+ResidualBuildaTwo-WayFitApplythefit-plus-residualframeworkiterativelyinbothdimensionsExample:LauverandJones’researchoncareer-self-efficacyCollectoccupationalpreferencedatafromethnicallydiversegroupsPercentageofstudentsconsideringthatcareeranoption1.2ProceduresofEDA——ModelFitBuildaTwo-WayFit:Example1.2ProceduresofEDA——ResidualAnalysisBuildaTwo-WayFit:Example1.2ProceduresofEDA——IterativeProcessUseRobustandResistantMethodsResistance;Smoothness;BreadthApproachestoassessresistance(breakdownpoint;trimean,etc.)PayAttentiontoOutliersCorrectmentalandcomputationalmodelsConsiderimportantdataandphenomenaoriginallyunanticipatedReexpresstheOriginalScalesTransformation(avoidradicalchangeofunderlyinginformation)Usualmethod(logarithmicscale;standardscore)PuttingitAllTogether

–

AnIterativeProcesstoFollowGraphics—Initialmodel,fit-plus-residual—ResidualAnalysis—Transformation,Outliers—Modificationofmodel(Iteratively)1.3ConclusionsEDAisFindpatternsinthedatatobuildrichmentalmodelsEspeciallyusefulwhenlittletheoreticalbackgroundavailablePromotetheorydevelopmentandtestingmendationsAWillingnesstoExplore;APhilosophyofYourOwn2.1FundamentalsWhatisMissingData?In

statistics,

missingvalues

occurwhenno

data

value

isstoredforthe

variable

inthecurrent

observation.(Wikipedia)TypeandPatternsofNonresponseUnitnonresponse←reweightingItemnonresponse←singleimputationWavenonresponse←MI(multipleimputation)&ML(maximumlikelihood)Univariatepattern(figure1)MonotonepatternArbitrarypattern2.1Fundamentals2.2OlderMethodsCaseDeletionDiscardunitwhoseinformationispleteSimplicity;GenerallyvalidonlyunderMCAR;inefficiencySingleImputationImputingunconditionalmeansImputingfromunconditionaldistributionsImputingconditionalmeansImputingfromaconditionaldistribution

2.3MLEstimationML(maximumlikelihood)MLestimatesarenotsubstantiallybiasedunderMCARorMARbutarequitebiasedunderMNARAssumingthesampleislargeenoughDependingontheparticularapplicationAssumingunderMARconditionSoftwareforMLEstimationBMDP;SPSS;EMCOV;NORM;SAS;Mplus;S-PLUS;LISREL;Amelia

2.4MultipleImputation2.4MultipleImputationFeaturesofMIRelyingonlarge-sampleapproximationsRequiringassumptionsaboutthedistributionofmissingnessMissingvaluesforeachparticipantarepredictedfromhisorherownobservedvaluesThejointrelationshipsamongthevariablesmustbeestimatedfromallavailabledataingroupMISoftwareNORMSASprocedure:PROCMIS-PLUSAmelia

3.1GravitationtoJobsCommensuratewithAbilityGravitationalHypothesisIndividuals,overthecourseoftheirlabormarketexperiences,willsortthemselvesintojobscompatiblewiththeirinterests,valuesandabilities.Goodperson-jobfitConceptsofFitIndividual’sbeliefandorganization’scultureIndividual’sabilityandabilityrequirementsforjobTwoTestsDirectionswithDifferentDatabaseLongitudinal–individual–directtestCross-sectional–job–indirecttest3.2Study1——LongitudinalTestHypothesisOvertime,lowerabilitypeoplewillgravitateintolowercomplexityjobsandhigherabilitypeoplewillgravitateintohighercomplexityjobsParticipantsDatafromNLSYdatabase;asampleof3887participantsValidscoresforASVABsubtest;occupationcodesVariablesAge(controlvariable)Cognitiveability(“g”fromASVABsubtestscores)Jobcomplexityin1982&1987OAPMap:sortjobsin13categoriesanddifferentiatebetweenjobsonthebasisofcognitiveabilityrequiredtoperformthejobto10levels3.2Study1——LongitudinalTestsOAPMap3.2Study1——LongitudinalTestsResultsSupportGravitationalHypothesis(Table2)IndividualsmovinglowerinthehierarchyovertimeshouldhavelowerabilityscoresthanthosewhoremainatthesamelevelorthosewhoproceedupwardThosemovinghigherhavethehighestmeangscoresCognitiveability(gscore)isasignificantpredictorofOAPmap(Table3)3.3Study2——Cross-sectionalTestsHypothesisAmoreexperiencedgroupofemployeesinaparticularjobwillexhibitlessvariabilityincognitiveabilitythanalessexperiencedgroupParticipantsDatafromUSES;asampleof60job-firmcombinationfor6051participantsValiddataforbothfirmandjobexperience;GATBscoresVariablesCognitiveability(GATBabilityscores)Firmandjobexperience(USES,self-reported)Jobcomplexity(5-categorysystemdevelopedbyJohnHuster)ResultBothfirmandjobexperiencearesignificantlyrelatedtovarianceofcognitiveabilityLess-experiencedgrouptendtohavelargervariability3.4ConclusionGravitationtoJobsCommensurateAbilityIndividualwithhighercognitiveabilitymoveintojobsrequiringmorecognitiveability.Groupshigherinbothfirmandjobexperiencehavesmallervariance.

Whatcouldwelearnfromthispaper?Twodifferentapproachestotesthypothesis:longitudinalandcross-sectionalDirectandindirecttestsDifferentdatabasetouse4.1DataAnalyticTrendsandTraininginStrategicManagementHittetal.’sassertion“Strategicmanagementresearchismovingbeyondcross-sectional,multipleregressionapproachestomethodsmoreattunedtothespecificproblemsandissueslikelytoinfluencestrategyresearch,suchasnetworkanalysis,eventstudies,andPoisson/negativebinomialregression”Doctoralstudentsshouldbetrainedincertainspecializedmethodratherthantraditionalmethod

ATwo-studyDesignTracktrendsintheuseofdataanalytictechniquesUnderstandthelevelofmasteryrecentdoctoralgraduatespossesswithbothtraditionalandspecializedmethods4.2Study1——DataAnalyticTrendsSampleandDataArticlespublishedinStrategicManagementJournal(SMJ)from1980to2001Asampleof297presentedoriginalempiricalstudiesCodeandgroupanalyticmethodsindifferentcategorizations4.2Study1——DataAnalyticTrends4.2Study1——DataAnalyticTrendsSampleandDataArticlespublishedinStrategicManagementJournal(SMJ)from1980to2001Asampleof297presentedoriginalempiricalstudiesCodeandgroupanalyticmethodsindifferentcategorizationsResultsBasictechniquesfalloutoffavor(e.g.testofmeans)GLMremainthedominantgroupoftechniques(e.g.multipleregressionandhierarchicalregression)Specializedtechniquesgrowinuse4.3Study2——PhDTrainingSampleandData77strategicmanagementPhDresearchers“Whenyouleftgraduateschool,howcompetentwereyouwitheachmethod?”/“Towhatextentareyoucompetentnowwiththesemethods?”Collectanswers(scales1-5)from1996to20014.3Study2——PhDTraining4.3Study2——PhDTrainingSampleandData77strategicmanagementPhDresearchersbetween1996to2001“Whenyouleftgraduateschool,howcompetentwereyouwitheachmethod?”/“Towhatextentareyoucompetentnowwiththesemethods?”Collectanswers(scales1-5)from1996to2001ResultsTraditionaltechniques:welltrainedSpecializedtechniques:notimprovedsincegraduationMorerecentgraduatesleftgraduateschoolpossessmoreconfidencesometechniquesthenearliergraduates4.4ConclusionDataAnalyticMethodTrendRiseofsomespecializedtechniquesRelianceofregressionmodelManyresearchersarenotfullyexploitingtheirdataDoctoralTrainingPhDgraduatesarecompetentwithacoresetoftechniquesInadequatetrainingforvitalmethodsofcurrentandfutureknowledgedevelopmentDoctoralprogramsshouldworktoclosethegapbetweenwhatstudentsknowandwhattheyneedtoknow5.1RightsandObligationsoftheRespondentRightsoftheRespondentPrivacyBeinginformedObligationsoftheRespondentBeingtruthful5.2RightsandObligationsoftheClientSponsorRightsoftheClientSponsorPrivacyBeinginformedObligationsoftheClientSponsorObservinggeneralbusinessethicswhendealingwithresearchsuppliersAvoidingmisusingtheresearchfindingstosupportitsaimsRespectingresearchsubjects’privacyBeingopenaboutitsintentionsandbusinessproblems5.3RightsandObligationsoftheResearcherRightsoftheResearcherBeinginformedObligationsoftheResearcherAdheringtothepurposeoftheresearchMaintainingobjectivityAvoidingmisrepresentingresearchfindingsProtectingsubjectsandclients’righttoconfidentialityAvoidingshadingresearchconclusions6FromtheEditors:anEthicalQuizScenario1:PlagiarismWhatdoyouthinkaboutthefollowingbehaviors?ReusingadescriptionofasampleyouwroteforanotherpaperReusingadescriptionofascaleyouusedinanotherpaperThe“codeofethical”ofAOMAOMmembersexplicitlyciteothers’workorideas,includingtheirown,eveniftheworkorideasarenotquotedverbatimorparaphrased.Scenario2:Data(Re)useThe“codeofethical”ofAOMWhenAOMmemberspublishdataorfindingsthatoverlapwithworktheyhavepreviouslypublishedelsewhere,theycitethesepublications,andtheymustsendthepriorpublicationworktotheAOMjournaleditors.6FromtheEditors:anEthicalQuizScenario3:InstitutionalReviewBoard(IRB)The“codeofethical”ofAOMWhenAOMmembersconductresearch,theyshouldobtaintheinformedconsentoftheindividualsScenario4:CoauthorsWhatdoyouthinkaboutthefollowingbehaviors?SubmittingapapertoajournalorconferencewithoutallofthecoauthorsbeingawareofitAddingacoauthorwithoutgettingthepermissionofthosealreadyonthepaperThe“cod