结构方程模型张伟雄PPT课件.ppt

1 RunningLISRELMeasurementModelLISREL测量模型 张伟雄博士香港中文大学工商管理学院副院长管理学系教授GordonW Cheung Ph DProfessor DepartmentofManagementAssociateDean FacultyofBusinessAdministrationTheChineseUniversityofHongKong 2 MeasurementModel ConfirmatoryFactorAnalysisModel 5 6 7 8 82 72 62 52 12 3 ScalingofLatentVariable设定因子的度量单位 Virtuallyalllatentvariableshaveambiguousscales thescalechoiceislargelyarbitrary Aconvenientchoiceistogivethesamescaleasinthesensethatiszeroandisone Anotherchoiceistostandardizethelatentvariablessuchthat 将所有因子的方差固定为1 Bollen 1989 StructuralEquationswithLatentVariables Pp 153 Wiley 4 RunningLISRELProgram Titleline 标题 InputSpecification 输入设定 ModelSpecification 模型设定 OutputSpecification 输出设定 5 InputSpecification DANI k numberofindicators NO numberofcasesLA labelsfortheobservedvariables V1V2V3V4V5RAFI filename ofrawdata CMFI filename ofcovariancematrix KMFI filename ofcorrelationmatrix SDFI filename ofstandarddeviations MEFI filename ofobservedmeans SE SelectandSequence 54321 6 ModelSpecification I MONY 2NX 3NE 1NK 2LY FILX FIBE FIGA FRPH SY FRPS DI FRTE DI FRTD DI FRTY FITX FITX FIKA FILKksilabelsLEetalabels 7 ModelSpecification II FRlistofparameterstobeestimatedFIlistofparameterstohavespecifiedvaluesVAnumericalvalueofFIparametersCOparametermatrixelement expressionwithotherparametersIRlistofparametermatrixelements number numberEQparametervaluesconstrainedtobeequalSTstartingvalues 8 OutputSpecification PathDiagramOUAD OFFND 4MI 9 Exercise CFAModel Drawthediagram 10 Exercise CFAModel Drawthediagram 11 InputSpecification Titleline ExerciseDANI 8NO 200LAV1V2V3V4V5V6V7V8CM1 650 451 140 350 301 010 510 490 431 580 070 200 200 230 750 170 140 170 270 230 650 410 200 070 210 110 250 850 220 230 260 360 240 250 160 75 12 ModelSpecification MONX 8NK 2LX FILKStaffCustomerVA1LX11LX52FRLX21LX31LX41FRLX62LX72LX82PDOUAD OFFND 4MI 13 ReadingLISRELOutput Checkforunreasonableparameters 不正常参数 ModelfitParameterestimates 参数估值 MultiplecorrelationCFA1 ls8 14 Identification Degreetowhichthereisasufficientnumberofequationsto solvefor eachoftheparameterstobeestimated NumberofknownparametersNumberofestimatedparametersqDegreesofFreedom 自由度 df p qCFA1 GIF 15 Goodness of Fit拟合优度 16 MinimumFitFunctionValues拟合函数 17 Chi SquareTest Wheng 1 18 Relativechi square 2to1or3to1areindicativeofanacceptablefit CarminesandMcIver 1981 p 80 19 RootMeanSquareErrorofApproximation Browne Cudeck 1993 EstimatedRMSEA pClose p valuefortestingthenullhypothesisthatthepopulationRMSEAisnogreaterthan 05 20 ComparisonstoaBaselineModel 1 Bentler Bonett 1980 normedfitindex NFI Bollen s 1986 relativefitindex RFI 21 ComparisonstoaBaselineModel 2 Bollen s 1989 incrementalfitindex IFI Tucker LewisNon normedfitindex NNFI 22 ComparisonstoaBaselineModel 3 McDonaldandMarsh s 1990 relativenoncentralityindex RNI Bentler 1990 comparativefitindex CFI 23 OtherFitIndices Goodnessoffitindex GFI AdjustedGoodnessoffitindex AGFI 24 FitIndicesforcomparisonacrossnon nestedmodels Akaike 1973 AkaikeInformationCriteria AIC Browne Cudeck 1989 BCCBayesInformationCriterion Schwarz 1978 Raftery 1993 25 FitIndicesforcomparisonacrossnon nestedmodels Bozdogan s 1987 CAICECVI 26 Hoelter sCriticalN Thelargestsamplesizeforwhichonewouldnotrejectthehypothesisthatamodeliscorrectatalpha 05 27 RootMeanSquareResidual RMR Thesquarerootoftheaveragesquaredamountbywhichthesamplevariancesandcovariancesdifferfromtheirestimatesobtainedundertheassumptionyourmodeliscorrect ThesmallertheRMRis thebetter 28 CFAExercise2 1 Thedatasetcontains204observationsof12continuousvariables The12variablesareindicatorsofthelatentvariablesSelf esteem DepressionandImpulsiveness The12variablesareobservedona5 pointLikertscale ThedatasetislistedinthefileCFA2 PSF 29 CFAExercise2 2 SELF1toSELF5areindicatorsofthelatentvariableSelf esteem DEPRES1toDEPRES4areindicatorsofthelatentvariableDepression IMPULS1toIMPULS3areindicatorsofthelatentvariableImpulsiveness PathDiagramCFA2 GIF 30 CFAExercise2 Output ProgramFileCFA2 ls8 31 NestedModels Whenoneormorefreeparametersofamodelareconstrained themodelthusconstrainedissaidtobenestedintheonefromwhichitwasderived Theconstrainedmodelusuallyhaveaworsefit Doestheimprovementinfitprovidedbyamorecomprehensivemodelwarrantpreferringittoamoreparsimoniousmodelnestedinit 32 LikelihoodRatioTest Problem sensitivetosamplesizeSolution CFIchangesinCFIlessthan 01Cheung Rensvold 2002 StructuralEquationModeling 9 233 255 33 ModificationIndices修正指数 Calculatedforeachconstrainedrelationshippossibleinaspecifiedmodel TheModificationIndex MI valueforaspecificconstrainedparameterindicatestheimprovementinoverallmodelfit reductioninChi squarestatistic thatispossibleifthatparameterisnotconstrained CFA2 ls8 Hair etal 1995 MultivariateDataAnalysiswithReadings Pp 620PrenticeHall 34 Second OrderFactorAnalysis高阶因子分析 Amoregeneralandabstractvariablemaydeterminethe first order latentvariables CFA3 GIF 2ord CFA 1 GIFThe fi