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1、§8.6因子分析的spss實(shí)現(xiàn)在前面,我們用SPSS的FactorAnalysis模塊實(shí)現(xiàn)了主成分分析,實(shí)際上,F(xiàn)actorAnalysis主要是SPSS軟件進(jìn)行因子分析的模塊,由于主成分分析與因子分析(特別是因子分析中的主成分法)之間有密切的關(guān)系,SPSS軟件將這兩種分析方法放到同一模塊中。下面我們先用SPSS軟件自帶的數(shù)據(jù)說明FactorAnalysis模塊進(jìn)行因子分析的方法,然后給出一個(gè)具體案例。為了與主成分分析進(jìn)行比較,我們此處仍延用SPSS自帶的Employeedata.sav數(shù)據(jù)集。【例8.6.1】數(shù)據(jù)集wxm6-5.sav中各變量解釋說明見上一章主成分分析,用Facto
2、rAnalysis模塊進(jìn)行因子分析。打開wxm6-5.sav數(shù)據(jù)集并依次選分析(Analyze)=>降維(Dimensionreduction)=>因子分析(Factor)打開因子分析(Factor)對話框:變量x1-x8進(jìn)入Variables窗口。點(diǎn)擊對話框右側(cè)的Extraction進(jìn)入Extration對話框,在Method選項(xiàng)框我們看到SPSS默認(rèn)是用主成分法提取因子,在Analyze框架中看到是從分析相關(guān)陣的結(jié)構(gòu)出發(fā)求解公因子。點(diǎn)Continue按鈕繼續(xù)。如果這樣交由程序運(yùn)行的話,將得到與主成分分析同樣的結(jié)果,其中包括公因子解釋方差的比例,因子載荷矩陣(即ComponentM
3、atrix)等。選中scores=>Displayfactorscorecoefficientmatrix復(fù)選框,它要求SPSS輸出因子得分矩陣,即標(biāo)準(zhǔn)化主成分(因子)用原始變量線性表示的系數(shù)矩陣。點(diǎn)Continue繼續(xù),點(diǎn)OK按鈕運(yùn)行,可以得到如下輸出結(jié)果:CommunalitiesInitialExtraction100m(秒)1.000.668200m(秒)1.000.752400m(秒)1.000.838800m(分)1.000.9001500m(分)1.000.9205000m(分)1.000.87910000m(分)1.000.891馬拉松(分)1.000.774Extr
4、actionMethod:PrincipalComponentAnalysis.TotalVarianceExplainedComponentInitialEigenvaluesExtractionSumsofSquaredLoadingsTotal%ofVarianceCumulative%Total%ofVarianceCumulative%16.62282.77782.7776.62282.77782.7772.87810.97093.7473.1591.99295.7394.1241.55197.2895.080.99998.2886.068.85099.1377.046.5
5、8099.7178.023.283100.000ExtractionMethod:PrincipalComponentAnalysis.ComponentMatrixaComponent1100m(秒).817200m(秒).867400m(秒).915800m(分).9491500m(分).9595000m(分).93810000m(分).944馬拉松(分).880ExtractionMethod:PrincipalComponentAnalysis.a.1componentsextracted.ComponentScoreCoefficientMatrixComponent110
6、0m(秒).123200m(秒).131400m(秒).138800m(分).1431500m(分).1455000m(分).14210000m(分).143馬拉松(分).133ExtractionMethod:PrincipalComponentAnalysis.ComponentScores.TotalVarianceExplainedComponentInitialEigenvaluesExtractionSumsofSquaredLoadingsTotal%ofVarianceCumulative%Total%ofVarianceCumulative%16.62282.777
7、82.7776.62282.77782.7772.87810.97093.747.87810.97093.7473.1591.99295.7394.1241.55197.2895.080.99998.2886.068.85099.1377.046.58099.7178.023.283100.000ExtractionMethod:PrincipalComponentAnalysis.ComponentMatrixaComponent12100m(秒).81