用SPSS Mixed Model 定义多水平模型

1、用SPSS Mixed Model 定义多水平模型北京师范大学心理学院刘红云数据结构 一个个体一行记录，多个变量，含有一个描述个体编号的变量 Multiple Variable Data Structure （MV） http:/ 一次观测一行记录，含有一个个体编号和测量次数或时间的变量 Multiple Record Data Structure （MR） /stat/examples/alda/ Multiple Variable Data StructureMultiple Record Data Structure 具有一般嵌套结构特点的多层

2、数据学生嵌套于学校句法（Syntax） GET FILE=C:HLM_EXAMPLEEX1.SAV. MIXED MATHACH BY SECTOR WITH MEANSES CSES /METHOD = REML /PRINT = SOLUTION TESTCOV /FIXED = MEANSES SECTOR CSES MEANSES*CSES SECTORCSES SSTYPE(3) /RANDOM = INTERCEPT CSES SUBJECT(SCHOOL) COVTYPE(UN).句法（Syntax）解释1 GET FILE=C:HLM_EXAMPLEEX1.SAV.2 MIXE

3、D MATHACH BY SECTOR WITH MEANSES CSES3 /METHOD = REML4 /PRINT = SOLUTION TESTCOV5 /FIXED = MEANSES SECTOR CSES MEANSES*CSES SECTORCSES SSTYPE(3)6 /RANDOM = INTERCEPT CSES SUBJECT(SCHOOL) COVTYPE(UN). 1 打开数据文件； 2 因变量为MATHACH，自变量为SECTOR ， MEANSES CSES，分类自变量写在BY的后面，连续自变量写在WITH的后面；3 用限制性极大似然估计法，在Mixed M

4、odel中估计方法有REML和ML两种，REML是缺省的设置；4SOLUTION定义打印输出固定部分参数估计和检验结果，TESTCOV要求打印输出随机部分协方差矩阵的估计和检验结果；5FIXED后面定义模型中的预测变量；6Random后的变量用来定义允许第二层有差异的随机变量，SUBJECT后的SCHOOL为更高的组变量， COVTYPE用来定义协方差矩阵的类型MIXED MODEL应用举例：模型1 无条件模型 GET FILE=C:HLM_EXAMPLEEX1.SAV. MIXED MATHACH /METHOD = REML /PRINT = SOLUTION TESTCOV /FIXED

5、 = |SSTYPE(3) /RANDOM = INTERCEPT | SUBJECT(SCHOOL) COVTYPE(UN).应用举例：模型1无条件模型参数估计结果Estimates of Fixed EffectsEstimates of Fixed Effectsa a12.63697.2443936156.64751.707.00012.154241913.1197058ParameterInterceptEstimateStd. ErrordftSig.Lower BoundUpper Bound95% Confidence IntervalDependent Variable: M

6、ATHACH.a. E Es st ti imm a at te es s o of f C Co ov va ar ri ia an nc ce e P Pa ar ra amm e et te er rs sa a39.14832 .660644759.258.000 37.8746616 40.46481338.61402481.07880367.985.0006.7391217 11.0105479ParameterResidualVarianceIntercept subject= SCHOOLEstimate Std. ErrorWald ZSig.Lower BoundUpper

7、 Bound95% Confidence IntervalDependent Variable: MATHACH.a. 应用举例：模型2条件模型（水平2预测变量） MIXED MATHACH with meanses /METHOD = REML /PRINT = SOLUTION TESTCOV /FIXED = MEANSES|SSTYPE(3) /RANDOM = INTERCEPT | SUBJECT(SCHOOL) COVTYPE(UN).应用举例：模型2条件模型（水平2预测变量）结果E Es st ti imma at te es s o of f F Fi ix xe ed d

8、E Ef ff fe ec ct ts sa a12.64944.1492801153.74384.736.00012.354530312.94434045.8635385.3614580153.40716.222.0005.14946066.5776163ParameterInterceptMEANSESEstimateStd. ErrordftSig.Lower BoundUpper Bound95% Confidence IntervalDependent Variable: MATHACH.a. Estimates of Covariance ParametersEstimates o

9、f Covariance Parametersa a39.15708.660801659.257.00037.883119540.47388642.6387080.40433866.526.0001.95415363.5630668ParameterResidualVarianceIntercept subject= SCHOOLEstimateStd. ErrorWald ZSig.Lower BoundUpper Bound95% Confidence IntervalDependent Variable: MATHACH.a. 应用举例：模型3条件模型（水平1预测变量中心化） MIXED

10、 MATHACH with cses /METHOD = REML /PRINT = SOLUTION TESTCOV /FIXED = CSES|SSTYPE(3) /RANDOM = INTERCEPT cses| SUBJECT(SCHOOL) COVTYPE(UN).应用举例：模型3条件模型（水平1预测变量中心化）结果Estimates of Fixed EffectsEstimates of Fixed Effectsa a12.64934.2445133156.75151.733.00012.166372713.13230482.1931921.1282588155.21817.1

11、00.0001.93983412.4465501ParameterInterceptCSESEstimateStd. ErrordftSig.Lower BoundUpper Bound95% Confidence IntervalDependent Variable: MATHACH.a. E Es st ti imma at te es s o of f C Co ov va ar ri ia an nc ce e P Pa ar ra amme et te er rs sa a36.70020.625744058.650.00035.494026937.94735498.68164341

12、.07962598.041.0006.803757111.0778399.0507473.4063926.125.901-.7457676.8472623.6939945.28078582.472.013.31402571.5337226ParameterResidualUN (1,1)UN (2,1)UN (2,2)Intercept + CSESsubject = SCHOOLEstimateStd. ErrorWald ZSig.Lower BoundUpper Bound95% Confidence IntervalDependent Variable: MATHACH.a. 应用举例

13、：模型4同时含有水平1和水平2的预测变量 MIXED MATHACH BY SECTOR WITH MEANSES CSES /METHOD = REML /PRINT = SOLUTION TESTCOV /FIXED = MEANSES SECTOR CSES MEANSES*CSES SECTOR*CSES |SSTYPE(3) /RANDOM = INTERCEPT CSES |SUBJECT(SCHOOL) COVTYPE(UN).应用举例：模型4同时含有水平1和水平2的预测变量结果Estimates of Fixed EffectsEstimates of Fixed Effect

14、sb b13.33026.2201540141.62760.550.00012.895044413.76546985.3391182.3692988150.97014.457.0004.60945696.0687796-1.21667.3063854149.600-3.971.000-1.8220739-.61127060a0.1.2961798.1729351147.6717.495.000.95443261.63792691.0388706.2989010160.5623.476.001.44858621.62915501.6425829.2397914143.3536.850.0001.

15、16859902.11656670a0.ParameterInterceptMEANSESSECTOR=0SECTOR=1CSESMEANSES * CSESCSES(SECTOR=0)CSES(SECTOR=1)EstimateStd. ErrordftSig.Lower BoundUpper Bound95% Confidence IntervalThis parameter is set to zero because it is redundant.a. Dependent Variable: MATHACH.b. Estimates of Covariance ParametersE

16、stimates of Covariance Parametersa a36.72113.626132758.648.00035.514210637.96906272.3818588.37174836.407.0001.75414243.2342021.1926034.2045243.942.346-.2082569.5934637.1013798.2138116.474.635.00162466.3262882ParameterResidualUN (1,1)UN (2,1)UN (2,2)Intercept + CSESsubject = SCHOOLEstimateStd. ErrorWald ZSig.Lower BoundUpper Bound95% Confidence Inte