多层统计分析模型：SAS与应用

MultilevelModels:AppficationsUsingSASiswritteninnontechnicalterms,focusesonthemethodsandapplicationsofvariousmultilevelmodels,includinglinermultilevelmodels,multilevellogisticregressionmodels,multilevelPoissonregressionmodels,multilevelnegativebinomialmodels,aswellassomecutting-edgeapplications,suchasmultilevelzero-inflatedPoisson(ZIP)model,randomeffectzero-inflatednegativebinomialmodel(RE-ZINB),mixed-effectmixed-distributionmodels,bootstrappingmultilevelmodels,andgroup-basedtrajectorymodels.Readerswilllearntobuildandapplymultilevelmodelsforhierarchicallystructuredcross-sectionaldataandlongitudinaldatausingtheinternationallydistributedsoftwarepackageStatisticsAnalysisSystem(SAS).DetailedSASsyntaxandoutputareprovidedformodelapplications,providingstudents,researchscientistsanddataanalystswithreadytemplatesfortheirapplications.

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Chapter1Introduction
1.1Conceptualframeworkofmultilevelmodeling
1.2Hierarchicallystructureddata
1.3Variablesinmultileveldata
1.4Analyticalproblemswithmultileveldata
1.5Advantagesandlimitationsofmultilevelmodeling
1.6Computersoftwareformultilevelmodeling
Chapter2BasicsofLinearMultilevelModels
2.1Intraclasscorrelationcoefficient(ICC)
2.2Formulationoftwo-levelmultilevelmodels
2.3Modelassumptions
2.4Fixedandrandomregressioncoefficients
2.5Cross-levelinteractions
2.6Measurementcentering
2.7Modelestimation
2.8Modelfit,hypothesistesting,andmodelcomparisons
2.8.1Modelfit
2.8.2Hypothesistesting
2.8.3Modelcomparisons
2.9Explainedlevel-1andlevel-2variances
2.10Stepsforbuildingmultilevelmodels
2.11Higher-levelmultilevelmodels
Chapter3ApplicationofTwo-levelLinearMultilevelModels
3.1Data
3.2Emptymodel
3.3Predictingbetween-groupvariation
3.4Predictingwithin-groupvariation
3.5Testingrandomlevel-1slopes
3.6Across-levelinteractions
3.7Otherissuesinmodeldevelopment
Chapter4ApplicationofMultilevelModelingtoLongitudinalData
4.1Featuresoflongitudinaldata
4.2Limitationsoftraditionalapproachesformodelinglongitudinaldata
4.3Advantagesofmultilevelmodelingforlongitudinaldata
4.4Formulationofgrowthmodels
4.5Datadescriptionandmanipulation
4.6Lineargrowthmodels
4.6.1Theshapeofaverageoutcomechangeovertime
4.6.2Randominterceptgrowthmodels
4.6.3Randominterceptandslopegrowthmodels
4.6.4Interceptandslopeasoutcomes
4.6.5Controllingforindividualbackgroundvariablesinmodels
4.6.6Codingtimescore
4.6.7Residualvariance/covariancestructures
4.6.8Time-varyingcovariates
4.7Curvilineargrowthmodels
4.7.1Polynomialgrowthmodel
4.7.2Dealingwithcollinearityinhigherorderpolynomialgrowthmodel
4.7.3Piecewise(linearspline)growthmodel
Chapter5MultilevelModelsforDiscreteOutcomeMeasures
5.1Introductiontogeneralizedlinearmixedmodels
5.1.1Generalizedlinearmodels
5.1.2Generalizedlinearmixedmodels
5.2SASProceduresformultilevelmodelingwithdiscreteoutcomes
5.3Multilevelmodelsforbinaryoutcomes
5.3.1Logisticregressionmodels
5.3.2Probitmodels
5.3.3Unobservedlatentvariablesandobservedbinaryoutcomemeasures
5.3.4Multilevellogisticregressionmodels
5.3.5Applicationofmultilevellogisticregressionmodels
5.3.6Applicationofmultilevellogitmodelstolongitudinaldata
5.4Multilevelmodelsforordinaloutcomes
5.4.1Cumulativelogitmodels
5.4.2Multilevelcumulativelogitmodels
5.5Multilevelmodelsfornominaloutcomes
5.5.1Multinomiallogitmodels
5.5.2Multilevelmultinomiallogitmodels
5.5.3Applicationofmultilevelmultinomiallogitmodels
5.6Multilevelmodelsforcountoutcomes
5.6.1Poissonregressionmodels
5.6.2Poissonregressionwithover-dispersionandanegativebinomialmodel
5.6.3MultilevelPoissonandnegativebinomialmodels
5.6.4ApplicationofmultilevelPoissonandnegativebinomialmodels
Chapter6OtherApplicationsofMultilevelModelingandRelatedIssues
6.1Multilevelzero-inflatedmodelsforcountdatawithextrazeros
6.1.1Fixed-effectZIPmodel
6.1.2Randomeffectzero-inflatedPoisson(RE-ZIP)models
6.1.3Randomeffectzero-inflatednegativebinomial(RE-ZINB)models
6.1.4ApplicationofRE-ZIPandRE-ZINBmodels
6.2Mixed-effectmixed-distributionmodelsforsemi-continuousoutcomes
6.2.1Mixed-effectsmixeddistributionmodel
6.2.2ApplicationoftheMixed-Effectmixeddistributionmodel
6.3Bootstrapmultilevelmodeling
6.3.1Nonparametricresidualbootstrapmultilevelmodeling
6.3.2Parametricresidualbootstrapmultilevelmodeling
6.3.3Applicationofnonparametricresidualbootstrapmultilevelmodeling
6.4Group-basedmodelsforlongitudinaldataanalysis
6.4.1Introductiontogroup-basedmodel
6.4.2Group-basedlogitmodel
6.4.3Group-basedzero-inflatedPoisson(ZIP)model
6.4.4Group-basedcensorednormalmodels
6.5Missingvaluesissue
6.5.1Missingdatamechanismsandtheirimplications
6.5.2Handlingmissingdatainlongitudinaldataanalyses
6.6Statisticalpowerandsamplesizeformultilevelmodeling
6.6.1Samplesizeestimationfortwo-leveldesigns
6.6.2Samplesizeestimationforlongitudinaldataanalysis
Reference