483 SEM coure beijing revised 24 August 2004.pptVIP免费

KTHAUSEMp.1结构方程模型及其应用侯傑泰©香港中文大學教育心理系使用時請著明出處KTHAUSEMp.2•100个分数:21,31,32,05,06,09,10,22,29,18,11,01,39,92,23,27,93,97,30,02,96,40,53,78,04,98,36,07,08,24,54,55,77,99,34,03,86,87,59,60,15,62,63,43,52,28,79,58,65,95,81,85,57,14,17,33,16,19,20,37,25,69,84,61,64,68,70,42,45,72,83,89,44,38,47,71,00,73,12,35,82,56,75,41,46,49,50,94,66,67,76,51,88,90,74,13,26,80,48,91均值M=53，标准差SD=15KTHAUSEMp.3100名学生在9个不同学科间的相关系数KTHAUSEMp.4KTHAUSEMp.5KTHAUSEMp.6KTHAUSEMp.7•检查模型的准确性和简洁性•拟合优度指数（goodnessoffitindex），简称为拟合指数、NNFI、CFI•df=[不重复元素,p(p+1)/2]–[估计参数]•在前面例子df=9x10/2–21=2422KTHAUSEMp.8KTHAUSEMp.9KTHAUSEMp.10KTHAUSEMp.11KTHAUSEMp.12KTHAUSEMp.13KTHAUSEMp.14KTHAUSEMp.15_________________________________________________________________________________________________模型dfNNFICFI需要估计的参数个数2______________________________________________________________________________________________M12440.973.98221=9Load＋9Uniq＋3CorrM227503.294.47118=9Load＋9UniqM326255.647.74519=9Load+9Uniq+1CorrM426249.656.75219=9Load＋9Uniq＋1CorrM527263.649.72718=9Load＋9Uniq624422.337.55821=9Load＋9Uniq＋3C721113.826.89824=9Load＋9Uniq＋6C______________________________________________________________________________________________KTHAUSEMp.16模型比较•自由度,拟合程度,不能保证最好，可能存在更简洁又拟合得很好的模型•Input:–相关（或协方差）矩阵–一个或多个有理据的可能模型•Output:–既符合某指定模型，又与差异最小的矩阵–估计各路径参数（因子负荷、因子相关系数等）。–计算出各种拟合指数ΣSSKTHAUSEMp.17结构方程模型的重要性•StructuralEquationModel，SEM•CovarianceStructureModeling，CSM•LInearStructuralRELationship，LISRELKTHAUSEMp.18结构方程模型的结构•测量模型δξΛxxεηΛyyx—外源指标（如6项社经指标）组成的向量。y—内生指标（如语、数、英成绩）组成的向量xΛ—因子负荷矩阵yΛδε—误差项•结构模型ζΓξΒηηKTHAUSEMp.19结构方程模型的优点•同时处理多个因变量•容许自变量和因变量含测量[误差...