1、實驗一：因子分析實驗目的：運用因子分析方法分析數據內容：SPSS操作因子分析案例背景：下表資料為25名健康人的7項生化檢驗結果，7項生化 檢驗指標依次命名為X1至X7,請對該資料進行因子分析，并對結 果進行解釋，實驗數據如下：X1X2X3X4X5X6X73.763.660.545.289.7713.744.788.594.991.3410.027.526.144.529.842.172.731.097.577.287.0712.661.792.10.829.037.082.5911.764.546.221.285.513.981.36.925.370

2、.620.443.367.638.848.398.7473.3111.683.534.761.129.649.491.0313.5713.1318.522.359.731.3319.879.8711.063.78.592.959.912.627.125.493.689.722.643.431.194.693.012.175.982.763.552.015.511.341.275.814.575.383.431.661.611.572.81.782.093.725.95.761.558.879.849.271.5113.69.0212.671.758.

3、394.922.5410.053.965.241.434.944.381.036.686.499.062.877.794.395.372.279.467.311.041211.5816.182.429.555.354.2511.742.773.511.054.944.524.58.071.792.11.298.213.082.429.13.754.661.729.416.445.1112.52.453.10.91實驗步驟：步驟一：1、導入數據確定數據類型3、輸入數據并確定分析方法 點擊 Analyze“Data Reduction”一 “Factor”打開 Variabl

4、es 歹U表框用箭頭按鈕從左邊列表框中選擇想要分析的變量名移動到右邊，準備分析。單擊DescriPtive按鈕，打開對話框，并在其中設置描述統計量（在需要得到的Continue:單擊EUontinueLance統計量前的括號打鉤）。選擇完畢，單擊continueM ethudxtraction按鈕打開對話框，進行因子設置。continueCoefficientsStatisticshJnivariate descriptiveInitial solutionCorre ation MatrixSignificance levelsDeterminantMethod: | Principal c

5、umpunents |-AnalyzeDisplay* Correldtion rnatriMCovariance matrixLance擇完畢后，單擊InverseReproducedAnti-image-Unrotated factor solutioniScree plot單擊Scores按鈕:進行因子得分選項設置。* Eigenvalues over: 1C ave as variables?RegressionC Bartlett(AndersLin-Fl ubinDisplay factor score coefficient matrix表格分析表（一）Descriptive S

6、tatisticsMeanStd. DeviationAnalysis NX17.10002.3238025X24.77322.4177925X32.34881.6655625X49.15243.0140525X55.45843.2734425X67.16724.5581725X72.34601.6109125表（二）Correlation Matrix（a）X1X2X3X4X5X6X7CorrelationX11.000.580.201.909.283.287-.533X2.5801.000.364.837.166.261-.608X3.201.3641.000.436-.704-.681-

7、.649X4.909.837.4361.000.163.203-.678X5.283.166-.704.1631.000.990.427X6.287.261-.681.203.9901.000.357X7-.533-.608-.649-.678.427.3571.000Sig. （1-tailed） X1.001.168.000.085.082.003X2.001.037.0001X3.168.037.015.000.000.000X4.000.000.000X5.085.214.000.218.000.017X6.082.104.000.165.

8、000.040X7.003.001.000.000.017.040a Determinant = 1.58E-007表（三）Inverse of Correlation MatrixX1X2X3X4X5X6X7X1173.581153.25562.639-325.473294.638-263.572-25.796X2153.255154.16652.636-302.580337.582-311.013-28.461X362.63952.63628.937-120.188102.956-85.762-10.578X4-325.473-302.580-120.188630.348-628.4005

9、63.81858.510X5294.638337.582102.956-628.400916.778-852.883-83.467X6-263.572-311.013-85.762563.818-852.883802.92074.148X7-25.796-28.461-10.57858.510-83.46774.14811.880表（四）KMO and Bartletts TestKaiser-Meyer-Olkin Measure of Sampling Adequacy.321Bartletts Test ofApprox. Chi-Square326.285Sphericitydf21S

10、ig.000表（五）Anti-image MatricesAnti-image Covariance X1.006.006X2.006.006X3.012.012X4-.003-.003X5.002.002X6-.002-.003X7-.013-.016Anti-image Correlation X1.292(a).937X2.937.268(a)X3.884.788X4-.984-.971X5.739.898X6-.706-.884X7-.568-.665X1X2X3X4.012-.003.002-.002-.0.012-.003.002-.003-.0.035-.007.004-.004

11、-.0-.007.002-.001.001.0.004-.001.001-.001-.0-.004.001-.001.001.0-.031.008-.008.008.0.884-.984.739-.706-.5.788-.971.898-.884-.6.350(a)-.890.632-.563-.5-.890.334(a)-.827.793.6.632-.827.306(a)-.994-.8-.563.793-.994.318(a).7-.570.676-.800.759.400a Measures of Sampling Adequacy(MSA)表(六)CommunalitiesIniti

12、alExtractionX11.000.797X21.000.773X31.000.859X41.000.980X51.000.983X61.000.976X71.000.834Extraction Method: Principal Component Analysis.表(七)Total Variance ExplainedComponentTotalInitial EigenvaluesExtraction Sums of Squared LoadingsRotatioTotal% of VarianceCumulative %Total% of VarianceCumulative %

13、13.39548.50348.5033.39548.50348.5033.30622.80640.09088.5932.80640.09088.5932.8953.4366.23694.8284.2763.94698.7755.0811.16099.9356.004.05999.9947.000.006100.000Extraction Method: Principal Component Analysis.表（八）Residual(a)Scree Plot4 3 2 1 0如=rau 卷LLJ1234567Component NumberComponent12X1.746.439X2.79

14、6.372X3.709-.597X4.911.389X5-.234.963X6-.177.972X7-.006.219Component Matrix3ExtrctiQii Method; Principal Component Analysis.a. 2 components extracted.表(十)Reproduced CorrelationsX1X2X3X4X5X6X1.797(b).777.237.870.297.343X2.777.773(b).342.870.172.221X3.237.342.859(b).413-.742-.706X4.870.870.413.980(b).

15、161.216X5.297.172-.742.161.983(b).978X6.343.221-.706.216.978.976(b)X7-.554-.624-.759-.722.419.370X1-.196-.036.039-.013-.057Reproduced CorrelationX7-.554-.624-.759-.722.419.370.834(b).021X2-.196.021-.033-.006.041X3-.036.021.023.037.025X4.039-.033.023.002-.013X5-.013-.006.037.002.012X6-.057.041.025-.0

16、13.012X7.021.016.110.044.008-.013.016.110.044.008-.013Extraction Method: Principal Component Analysis.a Residuals are computed between observed and reproduced correlations. There are 3 (14.0%) nonredundant residuals with absolute values greater than 0.05.b Reproduced communalitiesRotated Coinpoiiem

17、Matrix3Component12X1.070.161X2.070.033X3.421-.826X4.990.004X5.159.979X6.215.964X7-.732.547Extraction Method: Principal Component Analysis.Rotation Method: Varimax with Kaiser Normalization.a- Rotation converged in 3 iterations.Component Traiisformation MatrixComponent121.921-.3892.339.921Extraction

18、Method: Principal Component Analysis.Rotation Method: Varimax with Kaiser Normalization.Component Plot in Rotated SpaceX7X6O OX5X1X2 X3-1.0-0.50.00.51.0Component 1由表（十三）可知，X1, X2, X4, X5比較靠近兩個因子的坐標軸，表明分別用第一個因 子刻畫X4, X2，第二個引子刻畫X5，信息丟失較少，效果較好。三、分析從表（二）中可以看出，大部分的相關系數都較高，各變量成較強的線性關系， 能夠從中提取公共因子，適合進行因子分析。由表（四）可知，巴特利特球度檢驗的觀測值為326.285，相應的概率p接近0， 如果顯著水平a為0.05,由于概率p小于顯著水平a,應拒絕零假設，認為相關系 數矩陣與單位矩陣有顯著差別同時KMO值為0.882,根據Kaiser給出了 KMO 度量標準可知原有變量適合進行因子分析。表（六）指的是提取兩個特征根時的因子分析的初始解，由第二列可知，此時所 有變量的共同度均較高，各個變量的信息丟失均較少。由表