多元数据分析第一章课件—Chapter-01-US-7e-01.ppt

Copyright 2010PearsonEducation Inc publishingasPrentice Hall 1 1 Chapter1Introduction Copyright 2010PearsonEducation Inc publishingasPrentice Hall 1 2 LEARNINGOBJECTIVES Uponcompletingthischapter youshouldbeabletodothefollowing Explainwhatmultivariateanalysisisandwhenitsapplicationisappropriate Defineanddiscussthespecifictechniquesincludedinmultivariateanalysis Determinewhichmultivariatetechniqueisappropriateforaspecificresearchproblem Discussthenatureofmeasurementscalesandtheirrelationshiptomultivariatetechniques Describetheconceptualandstatisticalissuesinherentinmultivariateanalyses Chapter1Introduction Copyright 2010PearsonEducation Inc publishingasPrentice Hall 1 3 Whatisit MultivariateDataAnalysis allstatisticalmethodsthatsimultaneouslyanalyzemultiplemeasurementsoneachindividualorobjectunderinvestigation Whyuseit MeasurementExplanation PredictionHypothesisTesting WhatisMultivariateAnalysis Copyright 2010PearsonEducation Inc publishingasPrentice Hall 1 4 TheVariateMeasurementScalesNonmetricMetricMultivariateMeasurementMeasurementErrorTypesofTechniques BasicConceptsofMultivariateAnalysis Copyright 2010PearsonEducation Inc publishingasPrentice Hall 1 5 Thevariateisalinearcombinationofvariableswithempiricallydeterminedweights Weightsaredeterminedtobestachievetheobjectiveofthespecificmultivariatetechnique Variateequation Y W1X1 W2X2 WnXnEachrespondenthasavariatevalue Y TheY valueisalinearcombinationoftheentiresetofvariables Itisthedependentvariable PotentialIndependentVariables X1 incomeX2 educationX3 familysizeX4 TheVariate Copyright 2010PearsonEducation Inc publishingasPrentice Hall 1 6 TypesofDataandMeasurementScales Data MetricorQuantitative NonmetricorQualitative NominalScale OrdinalScale IntervalScale RatioScale Copyright 2010PearsonEducation Inc publishingasPrentice Hall 1 7 NonmetricNominal sizeofnumberisnotrelatedtotheamountofthecharacteristicbeingmeasuredOrdinal largernumbersindicatemore orless ofthecharacteristicmeasured butnothowmuchmore orless MetricInterval containsordinalproperties andinaddition thereareequaldifferencesbetweenscalepoints Ratio containsintervalscaleproperties andinaddition thereisanaturalzeropoint NOTE Thelevelofmeasurementiscriticalindeterminingtheappropriatemultivariatetechniquetouse MeasurementScales Copyright 2010PearsonEducation Inc publishingasPrentice Hall 1 8 Allvariableshavesomeerror Whatarethesourcesoferror Measurementerror distortsobservedrelationshipsandmakesmultivariatetechniqueslesspowerful Researchersusesummatedscales forwhichseveralvariablesaresummedoraveragedtogethertoformacompositerepresentationofaconcept MeasurementError Copyright 2010PearsonEducation Inc publishingasPrentice Hall 1 9 Inaddressingmeasurementerror researchersevaluatetwoimportantcharacteristicsofmeasurement Validity thedegreetowhichameasureaccuratelyrepresentswhatitissupposedto Reliability thedegreetowhichtheobservedvariablemeasuresthe true valueandisthuserrorfree MeasurementError Copyright 2010PearsonEducation Inc publishingasPrentice Hall 1 10 TypeIerror or istheprobabilityofrejectingthenullhypothesiswhenitistrue TypeIIerror or istheprobabilityoffailingtorejectthenullhypothesiswhenitisfalse Power or1 istheprobabilityofrejectingthenullhypothesiswhenitisfalse StatisticalSignificanceandPower Copyright 2010PearsonEducation Inc publishingasPrentice Hall 1 11 Effectsize theactualmagnitudeoftheeffectofinterest e g thedifferencebetweenmeansorthecorrelationbetweenvariables Alpha as issetatsmallerlevels powerdecreases Typically 05 Samplesize assamplesizeincreases powerincreases Withverylargesamplesizes evenverysmalleffectscanbestatisticallysignificant raisingtheissueofpracticalsignificancevs statisticalsignificance PowerisDeterminedbyThreeFactors Copyright 2010PearsonEducation Inc publishingasPrentice Hall 1 12 Copyright 2010PearsonEducation Inc publishingasPrentice Hall 1 13 ImpactofSampleSizeonPower Copyright 2010PearsonEducation Inc publishingasPrentice Hall 1 14 RulesofThumb1 1 1 1 1 H0 2 1 b 3 4 a n a 0 05 120 5 H0 m 120 H1 m 120 120 0 95 mH0 1 645 x s mH0 0 05 116 71 mH0 120 H0 H0 a 05 1 Hypotheses H0 m 120H1 m 120 Hypotheses H0 m 120H1 m 120 m1 112 mH0 120 H0 H0 a 05 2 m 1 112 m1 112 b 1 b 116 71 mH0 120 H0 H0 a 05 3 Hypotheses H0 m 120H1 m 120 m 1 112 m1 112 b 1 b 116 71 mH0 120 H0 H0 a 05 4 2 36 4 z z 12 36 116 71 112 Hypotheses H0 m 120H1 m 120 m 1 112 m1 112 b 1 b 116 71 mH0 120 H0 H0 a 05 5 z 2 355 b 0093 5b Hypotheses H0 m 120H1 m 120 m 1 112 m1 112 b 1 b 116 71 mH0 120 H0 H0 a 05 5 1 0 0093 9907 6 Hypotheses H0 m 120H1 m 120 50 1 00 75 25 H0 112 A 0 9907 50 1 00 75 25 112 C 9907 m H0 50 1 00 75 25 112 116 71 C D 5000 9907 m H0 50 1 00 75 25 112 116 71 C D 5000 9907 m H0 E 2595 118 Copyright 2010PearsonEducation Inc publishingasPrentice Hall 1 28 Dependencetechniques avariableorsetofvariablesisidentifiedasthedependentvariabletobepredictedorexplainedbyothervariablesknownasindependentvariables MultipleRegressionMultipleDiscriminantAnalysisLogit LogisticRegressionMultivariateAnalysisofVariance MANOVA andCovarianceConjointAnalysisCanonicalCorrelationStructuralEquationsModeling SEM TypesofMultivariateTechniques Copyright 2010PearsonEducation Inc publishingasPrentice Hall 1 29 Interdependencetechniques involvethesimultaneousanalysisofallvariablesintheset withoutdistinctionbetweendependentvariablesandindependentvariables PrincipalComponentsandCommonFactorAnalysisClusterAnalysisMultidimensionalScaling perceptualmapping CorrespondenceAnalysis TypesofMultivariateTechniques Copyright 2010PearsonEducation Inc publishingasPrentice Hall 1 30 SelectingaMultivariateTechnique Whattypeofrelationshipisbeingexamined dependenceorinterdependence Dependencerelationship Howmanyvariablesarebeingpredicted Whatisthemeasurementscaleofthedependentvariable Whatisthemeasurementscaleofthepredictorvariable Interdependencerelationship Areyouexaminingrelationshipsbetweenvariables respondents orobjects Copyright 2010PearsonEducation Inc publishingasPrentice Hall 1 31 TwoBroadTypesofMultivariateMethods Dependence analyzedependentandindependentvariablesatthesametime Interdependence analyzedependentandindependentvariablesseparately Copyright 2010PearsonEducation Inc publishingasPrentice Hall 1 32 MultipleRegressionandConjoint DiscriminantAnalysisandLogit MANOVAandCanonical CanonicalCorrelation DummyVariables Metric Nonmetric Metric Nonmetric Metric Nonmetric FactorAnalysis ClusterAnalysis NonmetricMDSandCorrespon denceAnalysis SelectingtheCorrectMultivariateMethod SEM CFA SeveralDependentVariables OneDependentVariable MetricMDS MultivariateMethods DependenceMethods InterdependenceMethods MultipleRelationships StructuralEquations Copyright 2010PearsonEducation Inc publishingasPrentice Hall 1 33 MultipleRegression asinglemetricdependentvariableispredictedbyseveralmetricindependentvariables Y X1 X2 Copyright 2010PearsonEducation Inc publishingasPrentice Hall 1 34 DiscriminantAnalysisWhatisit single non metric categorical dependentvariableispredictedbyseveralmetricindependentvariables Whyuseit ExamplesofDependentVariables Gender Malevs FemaleCulture USAvs OutsideUSAPurchasersvs Non purchasersMembervs Non MemberGood AverageandPoorCreditRisk Copyright 2010PearsonEducation Inc publishingasPrentice Hall 1 35 LogisticRegression Asinglenonmetricdependentvariableispredictedbyseveralmetricindependentvariables Thistechniqueissimilartodiscriminantanalysis butreliesoncalculationsmorelikeregression Copyright 2010PearsonEducation Inc publishingasPrentice Hall 1 36 MANOVA Severalmetricdependentvariablesarepredictedbyasetofnonmetric categorical independentvariables Copyright 2010PearsonEducation Inc publishingasPrentice Hall 1 37 CANONICALANALYSISSeveralmetricdependentvariablesarepredictedbyseveralmetricindependentvariables Copyright 2010PearsonEducation Inc publishingasPrentice Hall 1 38 isusedtounderstandrespondents preferencesforproductsandservices Indoingthis itdeterminestheimportanceofboth attributesandlevelsofattributes basedonasmallersubsetofcombinationsofattributesandlevels CONJOINTANALYSIS Copyright 2010PearsonEducation Inc publishingasPrentice Hall 1 39 TypicalApplications SoftDrinksCandyBarsCerealsBeerApartmentBuildings CondosSolvents CleaningFluids CONJOINTANALYSIS Copyright 2010PearsonEducation Inc publishingasPrentice Hall 1 40 StructuralEquationsModeling SEM Estimatesmultiple interrelateddependencerelationshipsbasedontwocomponents MeasurementModelStructuralModel Copyright 2010PearsonEducation Inc publishingasPrentice Hall 1 41 ExploratoryFactorAnalysis analyzesthestructureoftheinterrelationshipsamongalargenumberofvariablestodetermineasetofcommonunderlyingdimensions factors Copyright 2010PearsonEducation Inc publishingasPrentice Hall 1 42 groupsobjects respondents products firms variables etc sothateachobjectissimilartotheotherobjectsintheclusteranddifferentfromobjectsinalltheotherclusters ClusterAnalysis Copyright 2010PearsonEducation Inc publishingasPrentice Hall 1 43 ClusterAnalysisof EatingOut Questions Ieatatfastfoodrestaurantsatleastonceaweek Ipreferrestaurantswiththehighestqualityfood Ipreferrestaurantsthathavequickservice Iprefertoeatatrestaurantsthathaveaniceatmosphere Objective Identifygroupsthatmaximizeratioofbetweengroupsvariancelargewithingroupsvariancesmall 1 7 7 pointAgree DisagreeScale Copyright 2010PearsonEducation Inc publishingasPrentice Hall 1 44 Constructs TrustCommitmentCooperationLocusofControlJobSatisfactionTurnover High Medium Low OrganizationalCommitment Example ClusterAnalysis PolarExtremes removemiddlegroup s Copyright 2010PearsonEducation Inc publishingasPrentice Hall 1 45 MultidimensionalScaling identifies unrecognized dimensionsthataffectpurchasebehaviorbasedoncustomerjudgmentsof similaritiesorpreferencesandtransformstheseintodistancesrepresentedasperceptualmaps Copyright 2010PearsonEducation Inc publishingasPrentice Hall 1 46 CorrespondenceAnalysis usesnon metricdataandevaluateseitherlinearornon linearrelationshipsinanefforttodevelopaperceptualmaprepresentingtheassociationbetweenobjects firms products etc andasetofdescriptivecharacteristicsoftheobjects Copyright 2010PearsonEducation Inc publishingasPrentice Hall 1 47 EstablishPracticalSignificanceaswellasStatisticalSignificance SampleSizeAffectsAllResults KnowYourData StriveforModelParsimony LookatYourErrors ValidateYourResults GuidelinesforMultivariateAnalysis Copyright 2010PearsonEducation Inc publishingasPrentice Hall 1 48 Stage1 DefinetheResearchProblem Objectives andMultivariateTechnique s tobeUsedStage2 DeveloptheAnalysisPlanStage3 EvaluatetheAssumptionsUnderlyingtheMultivariateTechnique s Stage4 EstimatetheMultivariateModelandAssessOverallModelFitStage5 InterprettheVariate s Stage6 ValidatetheMultivariateModel AStructuredApproachtoMultivariateModelBu