(精心整理)三因素实验设计

三因素实验设计对三因素重复测量实验设计进行数据处理1、三因素完全随机实验设计数据处理过程：1、打开SPSS软件，点击DataView，进入数据输入窗口，将原始数据输入SPSS表格区域；2、在菜单栏中选择分析→一般线性模型→单变量；3、因变量DependentVariable方框中放入记忆成绩（JY），固定变量（FixedFactor(s)）方框中，放入自变量记忆策略、有无干扰和材料类型；4、点击选项（Options）按钮，选择Descriptivestatistics，对数据进行描述性统计；选择Homogeneitytests，进行方差齐性检验；5.结果分析：描述性统计量因变量:记忆成绩记忆策略有无干扰材料类型均值标准偏差N联想策略dimension2无干扰实物图片13.00001.581145图形图片8.00001.581145总计10.50003.0276510有干扰实物图片5.40002.073645图形图片4.6000.894435总计5.00001.5634710总计实物图片9.20004.3665410图形图片6.30002.1628210总计7.75003.6688620复述策略dimension2无干扰实物图片6.80001.303845图形图片7.20001.303845总计7.00001.2472210有干扰实物图片4.00001.000005图形图片2.8000.836665总计3.40001.0749710总计实物图片5.40001.8378710图形图片5.00002.5385910总计5.20002.1667320总计dimension2无干扰实物图片9.90003.5418110图形图片7.60001.4298410总计8.75002.8814320有干扰实物图片4.70001.7029410图形图片3.70001.2516710总计4.20001.5423820描述性统计量因变量:记忆成绩记忆策略有无干扰材料类型均值标准偏差N联想策略dimension2无干扰实物图片13.00001.581145图形图片8.00001.581145总计10.50003.0276510有干扰实物图片5.40002.073645图形图片4.6000.894435总计5.00001.5634710总计实物图片9.20004.3665410图形图片6.30002.1628210总计7.75003.6688620复述策略dimension2无干扰实物图片6.80001.303845图形图片7.20001.303845总计7.00001.2472210有干扰实物图片4.00001.000005图形图片2.8000.836665总计3.40001.0749710总计实物图片5.40001.8378710图形图片5.00002.5385910总计5.20002.1667320总计dimension2无干扰实物图片9.90003.5418110图形图片7.60001.4298410总计8.75002.8814320有干扰实物图片4.70001.7029410图形图片3.70001.2516710总计4.20001.5423820总计实物图片7.30003.7988920图形图片5.65002.3902220总计实物图片7.30003.7988920图形图片5.65002.3902220总计6.47503.2422540方差齐性检验结果：P=0.278>0.05所以各组数据方差齐性。误差方差等同性的Levene检验a因变量:记忆成绩Fdf1df2Sig.1.309732.278检验零假设，即在所有组中因变量的误差方差均相等。a.设计:截距+A+B+C+A*B+A*C+B*C+A*B*C被试间变量效应检验结果：A、B、C的主效应均极显著（P<0.01）；AB交互效应显著；AC交互效应极显著；BC交互效应不显著；ABC交互效应极显著。对于二阶与三阶交互效应显著的，还需进行简单效应与简单简单效应检验。主体间效应的检验因变量:记忆成绩源III型平方和df均方FSig.校正模型349.175a749.88226.254.000截距1677.02511677.025882.645.000A65.025165.02534.224.000B207.0251207.025108.961.000C27.225127.22514.329.001A*B9.02519.0254.750.037A*C15.625115.6258.224.007B*C4.22514.2252.224.146A*B*C21.025121.02511.066.002误差60.800321.900总计2087.00040校正的总计409.97539主体间效应的检验因变量:记忆成绩源III型平方和df均方FSig.校正模型349.175a749.88226.254.000截距1677.02511677.025882.645.000A65.025165.02534.224.000B207.0251207.025108.961.000C27.225127.22514.329.001A*B9.02519.0254.750.037A*C15.625115.6258.224.007B*C4.22514.2252.224.146A*B*C21.025121.02511.066.002误差60.800321.900总计2087.00040校正的总计409.97539a.R方=.852（调整R方=.819）简单效应检验：在主对话框中，单击Paste按钮，SPSS会把原先的全部操作转换成语句并粘贴到新打开的程序语句窗口中，在命令语句中加入EMMEANS引导的语句；结果：当被试使用联想策略进行记忆时，无干扰条件的记忆成绩极显著优于有干扰条件的记忆成绩；当被试使用复述策略进行记忆时，无干扰条件的记忆成绩也极显著优于有干扰条件的记忆成绩。当被试使用联想策略进行记忆时，实物图片的记忆成绩极显著优于图形图片的记忆成绩；当被试使用复述策略进行记忆时，实物图片与图形图片的记忆成绩无显著差异。简单简单效应检验：结果：所以a,b,c有显著差异。2、重复测量一个因素的三因素混合实验设计数据处理过程：1.DataView，进入数据输入窗口，将原始数据输入SPSS表格区域2.Analyze→GeneralLinearModel→RepeatedMeasures（在菜单栏中选择分析→一般线性模型→重复变量）3.在定义被试内变量（Within-SubjectFactorName）的方框中，设置被试内变量标记类型，在定义其水平（NumberofLevel）的对框中，输入3，表示有两个水平，然后按填加（Add）钮。4.按定义键（Define），返回重复测量主对话框，将b1、b2、b3选入被试内变量（Winthin-SubjectsVariables）方框中，将a、c选入被试间变量框中。5.点击选项Options，进行如下操作：①将被试内变量b（三个水平）键入到右边的方框中，采用[LSD（none）]法进行多重比较，②选择Descriptivestatistics命令，对数据进行描述性统计。选择Homogeneitytests进行方差齐性检验。6.单击continue选项，返回主对话框，点击OK，执行程序。7.结果：一元方差分析：标记类型主效应显著，F=37.022，P=0.009；句长类型主效应检验，因其满足球形假设，故参见每项检验的第一行SphericityAssumed的结果，即，F=47.79，P=.000，表明b变量主效应极其显著；a与b的交互效应检验。因其满足球形假设，故参见标准一元方差分析的结果，即F=34.02，P=.001，表明a与b的交互效应极显著。多重比较：长句与中句之间差异极其显著（P=0.003）；长句与短句之间差异极其显著（P=0.000）；中句与短句之间差异也极其显著（P=0.002）。描述性统计量有无干扰显示时间

均值标准偏差N实物图片dimension1无干扰dimension230秒14.2500.95743415秒9.75001.707834总计12.00002.725548有干扰dimension230秒5.2500.95743415秒6.50001.290994总计5.87501.246428总计dimension230秒9.75004.89168815秒8.12502.232078总计8.93753.7677416数字图片dimension1无干扰dimension230秒8.50001.29099415秒7.50001.290994总计8.00001.309318有干扰dimension230秒10.25001.70783415秒5.50001.290994描述性统计量有无干扰显示时间均值标准偏差N实物图片dimension1无干扰dimension230秒14.2500.95743415秒9.75001.707834总计12.00002.725548有干扰dimension230秒5.2500.95743415秒6.50001.290994总计5.87501.246428总计dimension230秒9.75004.89168815秒8.12502.232078总计8.93753.7677416数字图片dimension1无干扰dimension230秒8.50001.29099415秒7.50001.290994总计8.00001.309318有干扰dimension230秒10.25001.70783415秒5.50001.290994总计7.87502.900128总计dimension230秒9.37501.68502815秒6.50001.603578总计7.93752.1746616符号图片dimension1无干扰dimension230秒7.0000.81650415秒5.75001.707834总计6.37501.407898有干扰dimension230秒6.7500.95743415秒2.7500.957434总计4.75002.314558总计dimension230秒6.8750.83452815秒4.25002.052878总计7.87502.900128总计dimension230秒9.37501.68502815秒6.50001.603578总计7.93752.1746616符号图片dimension1无干扰dimension230秒7.0000.81650415秒5.75001.707834总计6.37501.407898有干扰dimension230秒6.7500.95743415秒2.7500.957434总计4.75002.314558总计dimension230秒6.8750.83452815秒4.25002.052878总计5.56252.0320416协方差矩阵等同性的Box检验aBox的M26.278F.749df118df2508.859Sig..760检验零假设，即观测到的因变量的协方差矩阵在所有组中均相等。a.设计:截距+a+c+a*c主体内设计:b多变量检验b多变量检验b效应值F假设df误差dfSig.bPillai的跟踪.80322.413a2.00011.000.000Wilks的Lambda.19722.413a2.00011.000.000Hotelling的跟踪4.07522.413a2.00011.000.000Roy的最大根4.07522.413a2.00011.000.000b*aPillai的跟踪.82225.414a2.00011.000.000Wilks的Lambda.17825.414a2.00011.000.000Hotelling的跟踪4.62125.414a2.00011.000.000Roy的最大根4.62125.414a2.00011.000.000b*cPillai的跟踪.1691.117a2.00011.000.362Wilks的Lambda.8311.117a2.00011.000.362Hotelling的跟踪.2031.117a2.00011.000.362Roy的最大根.2031.117a2.00011.000.362b*a*cPillai的跟踪.75216.698a2.00011.000.000Wilks的Lambda.24816.698a2.00011.000.000Hotelling的跟踪3.03616.698a2.00011.000.000Roy的最大根3.03616.698a2.00011.000.000a.精确统计量效应值F假设df误差dfSig.bPillai的跟踪.80322.413a2.00011.000.000Wilks的Lambda.19722.413a2.00011.000.000Hotelling的跟踪4.07522.413a2.00011.000.000Roy的最大根4.07522.413a2.00011.000.000b*aPillai的跟踪.82225.414a2.00011.000.000Wilks的Lambda.17825.414a2.00011.000.000Hotelling的跟踪4.62125.414a2.00011.000.000Roy的最大根4.62125.414a2.00011.000.000b*cPillai的跟踪.1691.117a2.00011.000.362Wilks的Lambda.8311.117a2.00011.000.362Hotelling的跟踪.2031.117a2.00011.000.362Roy的最大根.2031.117a2.00011.000.362b*a*cPillai的跟踪.75216.698a2.00011.000.000Wilks的Lambda.24816.698a2.00011.000.000Hotelling的跟踪3.03616.698a2.00011.000.000Roy的最大根3.03616.698a2.00011.000.000a.精确统计量b.设计:截距+a+c+a*c主体内设计:b主体内效应的检验度量:MEASURE_1源III型平方和df均方FSig.b采用的球形度96.167248.08329.974.000Greenhouse-Geisser96.1671.90250.54929.974.000Huynh-Feldt96.1672.00048.08329.974.000下限96.1671.00096.16729.974.000b*a采用的球形度78.000239.00024.312.000Greenhouse-Geisser78.0001.90241.00024.312.000Huynh-Feldt78.0002.00039.00024.312.000主体内效应的检验度量:MEASURE_1源III型平方和df均方FSig.b采用的球形度96.167248.08329.974.000Greenhouse-Geisser96.1671.90250.54929.974.000Huynh-Feldt96.1672.00048.08329.974.000下限96.1671.00096.16729.974.000b*a采用的球形度78.000239.00024.312.000Greenhouse-Geisser78.0001.90241.00024.312.000Huynh-Feldt78.0002.00039.00024.312.000下限78.0001.00078.00024.312.000b*c采用的球形度3.50021.7501.091.352Greenhouse-Geisser3.5001.9021.8401.091.350Huynh-Feldt3.5002.0001.7501.091.352下限3.5001.0003.5001.091.317

b*a*c采用的球形度54.500227.25016.987.000Greenhouse-Geisser54.5001.90228.64716.987.000Huynh-Feldt54.5002.00027.25016.987.000下限54.5001.00054.50016.987.001误差(b)采用的球形度38.500241.604Greenhouse-Geisser38.50022.8291.686Huynh-Feldt38.50024.0001.604下限78.0001.00078.00024.312.000b*c采用的球形度3.50021.7501.091.352Greenhouse-Geisser3.5001.9021.8401.091.350Huynh-Feldt3.5002.0001.7501.091.352下限3.5001.0003.5001.091.317b*a*c采用的球形度54.500227.25016.987.000Greenhouse-Geisser54.5001.90228.64716.987.000Huynh-Feldt54.5002.00027.25016.987.000下限54.5001.00054.50016.987.001误差(b)采用的球形度38.500241.604Greenhouse-Geisser38.50022.8291.686Huynh-Feldt38.50024.0001.604下限38.50012.0003.208简单效应检验：结果：无标记的情况下，各句子类型之间不存在显著性差异，F＝9.000，P=0.100；有标记的情况下，各句子类型之间存在极显著性差异，F＝150.333，P=0.007。三、重复测量两个因素的三因素混合实验设计数据处理过程：1.打开SPSS软件，点击DataView数据视图，进入数据输入窗口，将原始数据输入SPSS表格区域；2.在菜单栏中选择分析→一般线性模型→重复度量；3.分别定义两个被试内变量名及其水平数，点击“定义”；4、将b1c1、b1c2、b2c1、b2c2、b3c1、b3c2选入被试内变量（Winthin-SubjectsVariables）方框中，将a选入被试间变量框中；5、点击选项Options，然后将被试内变量b（三个水平）键入到右边的方框中，采用LSD（none）法进行多重比较，并选择描述统计和方差齐性检验，点击继续，再点击确定输出结果；6.结果：描述性统计结果：描述性统计量描述性统计量有无干扰均值标准偏差Nb1c1dimension1无干扰14.0000.925828有干扰4.8750.834528总计9.43754.7884416b1c2dimension1无干扰9.50001.195238有干扰6.12501.125998总计7.81252.0726416b2c1dimension1无干扰8.62501.060668有干扰10.00001.309318总计9.31251.3524716b2c2dimension1无干扰7.25001.281748有干扰5.50001.069048总计6.37501.4548816b3c1dimension1无干扰7.0000.755938有干扰6.8750.834528总计6.9375.7719016b3c2dimension1无干扰5.87501.246428有干扰2.8750.834528有无干扰均值标准偏差Nb1c1dimension1无干扰14.0000.925828有干扰4.8750.834528总计9.43754.7884416b1c2dimension1无干扰9.50001.195238有干扰6.12501.125998总计7.81252.0726416b2c1dimension1无干扰8.62501.060668有干扰10.00001.309318总计9.31251.3524716b2c2dimension1无干扰7.25001.281748有干扰5.50001.069048总计6.37501.4548816b3c1dimension1无干扰7.0000.755938有干扰6.8750.834528总计6.9375.7719016b3c2dimension1无干扰5.87501.246428描述性统计量有无干扰均值标准偏差Nb1c1dimension1无干扰14.0000.925828有干扰4.8750.834528总计9.43754.7884416b1c2dimension1无干扰9.50001.195238有干扰6.12501.125998总计7.81252.0726416b2c1dimension1无干扰8.62501.060668有干扰10.00001.309318总计9.31251.3524716b2c2dimension1无干扰7.25001.281748有干扰5.50001.069048总计6.37501.4548816b3c1dimension1无干扰7.0000.755938有干扰6.8750.834528总计6.9375.7719016b3c2dimension1无干扰5.87501.246428有干扰2.8750.834528有干扰2.8750.834528总计4.37501.8574216Box’s方差齐性结果：P=0.395>0.05，所以各组数据方差齐性。协方差矩阵等同性的Box检验aBox的M42.802F1.053df121df2720.888Sig..395检验零假设，即观测到的因变量的协方差矩阵在所有组中均相等。a.设计:截距+a主体内设计:b+c+b*c多变量检验：因为P=0<0.01，所以B的主效应极显著；而且P=0<0.01，BA的交互作用极显著；同理可知：C的主效应极显著，CA的交互效应不显著，BCA的三阶交互效应极显著。多变量检验b效应值F假设df误差dfSig.bPillai的跟踪.90662.841a2.00013.000.000Wilks的Lambda.09462.841a2.00013.000.000Hotelling的跟踪9.66862.841a2.00013.000.000Roy的最大根9.66862.841a2.00013.000.000b*aPillai的跟踪.961160.414a2.00013.000.000Wilks的Lambda.039160.414a2.00013.000.000Hotelling的跟踪24.679160.414a2.00013.000.000Roy的最大根24.679160.414a2.00013.000.000cPillai的跟踪.909139.528a1.00014.000.000Wilks的Lambda.091139.528a1.00014.000.000多变量检验b效应值F假设df误差dfSig.bPillai的跟踪.90662.841a2.00013.000.000Wilks的Lambda.09462.841a2.00013.000.000Hotelling的跟踪9.66862.841a2.00013.000.000Roy的最大根9.66862.841a2.00013.000.000b*aPillai的跟踪.961160.414a2.00013.000.000Wilks的Lambda.039160.414a2.00013.000.000Hotelling的跟踪24.679160.414a2.00013.000.000Roy的最大根24.679160.414a2.00013.000.000cPillai的跟踪.909139.528a1.00014.000.000Wilks的Lambda.091139.528a1.00014.000.000Hotelling的跟踪9.966139.528a1.00014.000.000Roy的最大根9.966139.528a1.00014.000.

000c*aPillai的跟踪.003.043a1.00014.000.839Wilks的Lambda.997.043a1.00014.000.839Hotelling的跟踪.003.043a1.00014.000.839Roy的最大根.003.043a1.00014.000.839b*cPillai的跟踪.2341.991a2.00013.000.176Wilks的Lambda.7661.991a2.00013.000.176Hotelling的跟踪.3061.991a2.00013.000.176Roy的最大根.3061.991a2.00013.000.176b*c*aPillai的跟踪.82731.113a2.00013.000.000Wilks的Lambda.17331.113a2.00013.000.000Hotelling的跟踪4.78731.113a2.00013.000.000Roy的最大根4.78731.113a2.00013.000.000a.精确统计量Hotelling的跟踪9.966139.528a1.00014.000.000Roy的最大根9.966139.528a1.00014.000.000c*aPillai的跟踪.003.043a1.00014.000.839Wilks的Lambda.997.043a1.00014.000.839Hotelling的跟踪.003.043a1.00014.000.839Roy的最大根.003.043a1.00014.000.839b*cPillai的跟踪.2341.991a2.00013.000.176Wilks的Lambda.7661.991a2.00013.000.176Hotelling的跟踪.3061.991a2.00013.000.176Roy的最大根.3061.991a2.00013.000.176b*c*aPillai的跟踪.82731.113a2.00013.000.000Wilks的Lambda.17331.113a2.00013.000.000Hotelling的跟踪4.78731.113a2.00013.000.000Roy的最大根4.78731.113a2.00013.000.000a.精确统计量b.设计:截距+a主体内设计:b+c+b*c球形假设检验：被试内变量球形假设检验，由于c变量只有两个水平，所以不需要检验；b,b*c均满足球形假设。Mauchly的球形度检验b度量:MEASURE_1主体内效应Mauchly的W近似卡方dfSig.EpsilonaGreenhouse-GeisserHuynh-Feldt下限dimension1b.7643.5032.174.809.965.500c1.000.0000.1.0001.0001.000b*c.952.6422.725.9541.000.500检验零假设，即标准正交转换因变量的误差协方差矩阵与一个单位矩阵成比例。a.可用于调整显著性平均检验的自由度。在"主体内效应检验"表格中显示修正后的检验。b.设计:截距+a主体内设计:b+c+b*cLevene’s方差齐性检验结果：因为P>0.05，各组因变量方差齐性。误差方差等同性的Levene检验aFdf1df2Sig.b1c1.168114.688b1c2.009114.926b2c1.152114.702b2c2.453114.512b3c1.399114.538b3c2.610114.448检验零假设，即在所有组中因变量的误差方差均相等。a.设计:截距+a主体内设计:b+c+b*c被试间变量效应：因为P=0<0.01，A的主效应极显著。主体间效应的检验度量:MEASURE_1转换的变量:平均值源III型平方和df均方FSig.截距5221.50015221.5004716.194.000a170.6671170.667154.151.000误差15.500141.107b因素的多重比较结果：实物图片的记忆成绩显著优于数字图片和符号图片，数字图片，数字图片的记忆成绩显著优于符号图片。成对比较度量:MEASURE_1(I)b(J)b均值差值(I-J)标准误差Sig.a差分的95%置信区间a下限上限12.781*.163.000.4311.13132.969*.257.0002.4173.52121-.781*.163.000-1.131-.43132.188*.220.0001.7152.66031-2.969*.257.000-3.521-2.4172-2.188*.220.000-2.660-1.715基于估算边际均值*.均值差值在.05级别上较显著。a.对多个比较的调整：最不显著差别（相当于未作调整）。进行简单效应检验：因为BA交互效应显著，需进行简单效应检验；程序语句：结果截图：b*a描述性统计结果b*a配对比较结果进行简单简单效应检验：BCA三阶交互效应显著，还需进行简单简单效应检验。程序语句：在a水平下b*c交互效应配对比结果四、三因素重复测量实验设计数据处理过程：1.打开SPSS软件，点击DataView，进入数据输入窗口，将原始数据输入SPSS表格区域；2.在菜单栏中选择分析→一般线性模型→重复变量；3.在定义被试内变量（Within-SubjectFactorName）的方框中，设置被试内变量标记类型，在定义其水平（NumberofLevel）的对框中，输入3，表示有两个水平，然后按填加（Add）钮。4.将a1b3c1、a1b3c2、a2b1c1、a2b1c2、a2b2c1、a2b2c2、a2b3c1、a2b3c2等选入被试内变量（Winthin-SubjectsVariables）方框中，将a选入被试间变量框中；5.点击选项Options，然后将被试内变量b（三个水平）键入到右边的方框中，采用LSD（none）法进行多重比较，并选择描述统计和方差齐性检验，点击继续，再点击确定输出结果；6.结果:3个自变量之间两两都有显著差异，3者之间也有显著差异。描述性统计量均值标准偏差Na1b1c114.2500.957434a1b1c29.75001.707834a1b2c18.50001.290994a1b2c27.50001.290994a1b3c17.0000.816504a1b3c25.75001.707834a2b1c15.2500.957434a2b1c26.50001.290994a2b2c110.25001.707834a2b2c25.50001.290994a2b3c16.5000.577354a2b3c22.7500.957434多变量检验b效应值F假设df误差dfSig.aPillai的跟踪.95766.783a1.0003.000.004Wilks的Lambda.04366.783a1.0003.000.004多变量检验b效应值F假设df误差dfSig.aPillai的跟踪.95766.783a1.0003.000.004Wilks的Lambda.04366.783a1.0003.000.004Hotelling的跟踪22.26166.783a1.0003.000.004Roy的最大根22.26166.783a1.0003.000.004bPillai的跟踪.95018.841a2.0002.000.050Wilks的Lambda.05018.841a2.0002.000.050Hotelling的跟踪18.84118.841a2.0002.000.050Roy的最大根18.84118.841a2.0002.000.050cPillai的跟踪.90528.683a1.0003.000.013Wilks的Lambda.09528.683a1.0003.000.013Hotelling的跟踪9.56128.683a1.0003.000.013Roy的最大根9.56128.683a1.0003.000.013a*bPillai

的跟踪.98988.494a2.0002.000.011Wilks的Lambda.01188.494a2.0002.000.011Hotelling的跟踪88.49488.494a2.0002.000.011Roy的最大根88.49488.494a2.0002.000.011a*cPillai的跟踪.011.034a1.0003.000.866Wilks的Lambda.989.034a1.0003.000.866Hotelling的跟踪.011.034a1.0003.000.866Roy的最大根.011.034a1.0003.000.866b*cPillai的跟踪.5601.271a2.0002.000.440Wilks的Lambda.4401.271a2.0002.000.440Hotelling的跟踪1.2711.271a2.0002.000.440Roy的最大根1.2711.271a2.0002.000.440a*b*cPillai的跟踪.96931.265a2.0002.000.031Wilks的Lambda.03131.265a2.0002.000.031Hotelling的跟踪31.26531.265a2.0002.000.031Roy的最大根31.26531.265a2.0002.000.031a.精确统计量Hotelling的跟踪22.26166.783a1.0003.000.004Roy的最大根22.26166.783a1.0003.000.004bPillai的跟踪.95018.841a2.0002.000.050Wilks的Lambda.05018.841a2.0002.000.050Hotelling的跟踪18.84118.841a2.0002.000.050Roy的最大根18.84118.841a2.0002.000.050cPillai的跟踪.90528.683a1.0003.000.013Wilks的Lambda.09528.683a1.0003.000.013Hotelling的跟踪9.56128.683a1.0003.000.013Roy的最大根9.56128.683a1.0003.000.013a*bPillai的跟踪.98988.494a2.0002.000.011Wilks的Lambda.01188.494a2.0002.000.011Hotelling的跟踪88.49488.494a2.0002.000.011Roy的最大根88.49488.494a2.0002.000.011a*cPillai的跟踪.011.034a1.0003.000.866Wilks的Lambda.989.034a1.0003.000.866Hotelling的跟踪.011.034a1.0003.000.866Roy的最大根.011.034a1.0003.000.866b*cPillai的跟踪.5601.271a2.0002.000.440Wilks的Lambda.4401.271a2.0002.000.440Hotelling的跟踪1.2711.271a2.0002.000.440Roy的最大根1.2711.271a2.0002.000.440a*b*cPillai的跟踪.96931.265a2.0002.000.031Wilks的Lambda.03131.265a2.0002.000.031Hotelling的跟踪31.26531.265a2.0002.000.031Roy的最大根31.26531.265a2.0002.000.031a.精确统计量b.设计:截距主体内设计:a+b+c+a*b+a*c+b*c+a*b*cMauchly的球形度检验b度量:MEASURE_1主体内效应Mauchly的W近似卡方dfSig.EpsilonaGreenhouse-GeisserHuynh-Feldt下限dimension1a1.000.0000.1.0001.0001.000b.4521.5902.452.646.927.500c1.000.0000.1.0001.0001.000a*b.4121.7722.412.630.873.500a*c1.000.0000.1.0001.0001.000b*c.3142.3162.314.593.757.500a*b*c.3412.1522.341.603.786.500检验零假设，即标准正交转换因变量的误差协方差矩阵与一个单位矩阵成比例。a.可用于调整显著性平均检验的自由度。在"主体内效应检验"表格中显示修正后的检验。b.设计:截距主体内设计:a+b+c+a*b+a*c+b*c+a*b*c主体内效应的检验度量:MEASURE_1源III型平方和df均方FSig.a采用的球形度85.333185.33366.783.004Greenhouse-Geisser85.3331.00085.33366.783.004Huynh-Feldt85.3331.00085.33366.783.004下限85.3331.00085.33366.783.004误差(a)采用的球形度3.83331.278Greenhouse-Geisser3.8333.0001.278Huynh-Feldt3.8333.0001.278下限3.8333.0001.278b采用的球形度100.042250.02146.471.000Greenhouse-Geisser100.0421.29277.45646.471.002Huynh-Feldt100.0421.85353.97746.471.000下限100.0421.000100.04246.471.006主体内效应的检验度量:MEASURE_1源III型平方和df均方FSig.a采用的球形度85.333185.33366.783.004Greenhouse-Geisser85.3331.00085.33366.783.004Huynh-Feldt85.3331.00085.33366.783.004下限85.3331.00085.33366.783.004误差(a)采用的球形度3.83331.278Greenhouse-Geisser3.8333.0001.278Huynh-Feldt3.8333.0001.278下限3.8333.0001.278b采用的球形度100.042250.02146.471.000Greenhouse-Geisser100.0421.29277.45646.471.002Huynh-Feldt100.0421.85353.97746.471.000下限100.0421.000100.04246.471.006误差(b)采用的球形度6.45861.076Greenhouse-Geisser6.4583.8751.667Huynh-Feldt6.4585.5601.162下限6.4583.0002.153c采用的球形度65.333165.33328.683.013Greenhouse-Geisser65.3331.00065.33328.683.013Huynh-Feldt65.3331.00065.33328.683.013下限65.3331.00065.33328.683.013误差(c)采用的球形度6.83332.278Greenhouse-Geisser6.8333.0002.278Huynh-Feldt6.8333.0002.278下限6.8333.0002.278a*b采用的球形度77.042238.52139.906.000Greenhouse-Geisser77.0421.26061.15639.906.003Huynh-Feldt77.0421.74644.11639.906.001下限77.0421.00077.04239.906.008误差(a*b)采用的球形度5.7926.965Greenhouse-Geisser5.7923.779

1.532Huynh-Feldt5.7925.2391.105下限5.7923.0001.931a*c采用的球形度.0831.083.034.866Greenhouse-Geisser.0831.000.083.034.866Huynh-Feldt.0831.000.083.034.866下限.0831.000.083.034.866误差(a*c)采用的球形度7.41732.472Greenhouse-Geisser7.4173.0002.472Huynh-Feldt7.4173.0002.472下限7.4173.0002.472b*c采用的球形度3.29221.646.856.471Greenhouse-Geisser3.2921.1862.775.856.435Huynh-Feldt3.2921.5132.175.856.452下限3.2921.0003.292.856.423误差(b*c)采用的球形度11.54261.924Greenhouse-Geisser11.5423.5593.243Huynh-Feldt11.5424.5402.542下限11.5423.0003.847a*b*c采用的球形度53.292226.64614.264.005Greenhouse-Geisser53.2921.20644.20714.264.022Huynh-Feldt53.2921.57333.88714.264.011下限53.2921.00053.29214.264.033误差(a*b*c)采用的球形度11.20861.868Greenhouse-Geisser11.2083.6173.099Huynh-Feldt11.2084.7182.376误差(b)采用的球形度6.45861.076Greenhouse-Geisser6.4583.8751.667Huynh-Feldt6.4585.5601.162下限6.4583.0002.153c采用的球形度65.333165.33328.683.013Greenhouse-Geisser65.3331.00065.33328.683.013Huynh-Feldt65.3331.00065.33328.683.013下限65.3331.00065.33328.683.013误差(c)采用的球形度6.83332.278Greenhouse-Geisser6.8333.0002.278Huynh-Feldt6.8333.0002.278下限6.8333.0002.278a*b采用的球形度77.042238.52139.906.000Greenhouse-Geisser77.0421.26061.15639.906.003Huynh-Feldt77.0421.74644.11639.906.001下限77.0421.00077.04239.906.008误差(a*b)采用的球形度5.7926.965Greenhouse-Geisser5.7923.7791.532Huynh-Feldt5.7925.2391.105下限5.7923.0001.931a*c采用的球形度.0831.083.034.866Greenhouse-Geisser.0831.000.083.034.866Huynh-Feldt.0831.000.083.034.866下限.0831.000.083.034.866误差(a*c)采用的球形度7.41732.472Greenhouse-Geisser7.4173.0002.472Huynh-Feldt7.4173.0002.472下限7.4173.0002.472b*c采用的球形度3.29221.646.856.471Greenhouse-Geisser3.2921.1862.775.856.435Huynh-Feldt3.2921.5132.175.856.452下限3.2921.0003.292.856.423误差(b*c)采用的球形度11.54261.924Greenhouse-Geisser11.5423.5593.243主体内效应的检验度量:MEASURE_1源III型平方和df均方FSig.a采用的球形度85.333185.33366.783.004Greenhouse-Geisser85.3331.00085.33366.783.004Huynh-Feldt85.3331.00085.33366.783.004下限85.3331.00085.33366.783.004误差(a)采用的球形度3.83331.278Greenhouse-Geisser3.8333.0001.278Huynh-Feldt3.8333.0001.278下限3.8333.0001.278b采用的球形度100.042250.02146.471.000Greenhouse-Geisser100.0421.29277.45646.471.002Huynh-Feldt100.0421.85353.97746.471.000下限100.0421.000100.04246.471.006误差(b)采用的球形度6.45861.076Greenhouse-Geisser6.4583.8751.667Huynh-Feldt6.4585.5601.162下限6.4583.0002.153c采用的球形度65.333165.33328.683.013Greenhouse-Geisser65.3331.00065.33328.683.013Huynh-Feldt65.3331.00065.33328.683.013下限65.3331.00065.33328.683.013误差(c)采用的球形度6.83332.278Greenhouse-Geisser6.8333.0002.278Huynh-Feldt6.8333.0002.278下限6.8333.0002.278a*b采用的球形度77.042238.52139.906.000Greenhouse-Geisser77.0421.26061.15639.906.003Huynh-Feldt77.0421.74644.11639.906.001下限77.0421.00077.04239.906.008误差(a*b)采用的球形度5.7926.965Greenhouse-Geisser5.7923.7791.532Huynh-Feldt5.7925.2391.105下限5.7923.0001.931a*c采用的球形度.0831.083.034.866Greenhouse-Geisser.0831.000.083.034.866Huynh-Feldt.0831.000.083.034.866下限.0831.000.083.034.866误差(a*c)采用的球形度7.41732.472Greenhouse-Geisser7.4173.0002.472Huynh-Feldt7.4173.0002.472下限7.4173.0002.472b*c采用的球形度3.29221.646.856.471Greenhouse-Geisser3.2921.1862.775.856.435Huynh-Feldt3.2921.5132.175.856.452下限3.2921.0003.292.856.423误差(b*c)采用的球形度11.54261.924Greenhouse-Geisser11.5423.5593.243Huynh-Feldt11.5424.5402.542下限11.5423.0003.847a*b*c采用的球形度53.292226.64614.264.005Greenhouse-Geisser53.2921.20644.20714.264.022Huynh-Feldt53.2921.57333.88714.264.011下限53.2921.00053.29214.264.033误差(a*b*c)采用