SEM处理过程及分析

一．测量模型

0.09 wc1 1.00 WC wc2 0.10 0.90 wc3 0.95 0.08 wf1 0.91 WF 1.00 wf2 -0.02 1.18 gm1 0.09 1.00 gm2 0.12 GM 1.00 0.12 gm3 0.92 gz1 0.14 1.00 GZ gz2 0.10 0.94 gz3 0.09 0.93

Degrees of Freedom = 38

Minimum Fit Function Chi-Square = 204.78 (P = 0.0)

Normal Theory Weighted Least Squares Chi-Square = 192.51 (P = 0.0) Estimated Non-centrality Parameter (NCP) = 154.51 90 Percent Confidence Interval for NCP = (114.88 ; 201.67) Minimum Fit Function Value = 1.47 Population Discrepancy Function Value (F0) = 1.11 90 Percent Confidence Interval for F0 = (0.83 ; 1.45)

Root Mean Square Error of Approximation (RMSEA) = 0.17 90 Percent Confidence Interval for RMSEA = (0.15 ; 0.20) P-Value for Test of Close Fit (RMSEA < 0.05) = 0.00 Expected Cross-Validation Index (ECVI) = 1.79 90 Percent Confidence Interval for ECVI = (1.50 ; 2.13) ECVI for Saturated Model = 0.95 ECVI for Independence Model = 31.50

Chi-Square for Independence Model with 55 Degrees of Freedom = 4356.65 Independence AIC = 4378.65

2.60 -3.398 2.80 4.48 2.78 -3.618 2.02 -3.58 2.96 2.94 Model AIC = 248.51 Saturated AIC = 132.00 Independence CAIC = 4422.01 Model CAIC = 358.87 Saturated CAIC = 392.15 Normed Fit Index (NFI) = 0.95 Non-Normed Fit Index (NNFI) = 0.94 Parsimony Normed Fit Index (PNFI) = 0.66 Comparative Fit Index (CFI) = 0.96 Incremental Fit Index (IFI) = 0.96 Relative Fit Index (RFI) = 0.93 Critical N (CN) = 42.52

Root Mean Square Residual (RMR) = 0.020 Standardized RMR = 0.0060 Goodness of Fit Index (GFI) = 0.80 Adjusted Goodness of Fit Index (AGFI) = 0.65 Parsimony Goodness of Fit Index (PGFI) = 0.46

首先生成测量模型后，点击output中的fit indices得到结果文件。如上，首先观察卡方为192.51（p小于0.05）显著。 其次非规准适配指标NNFI为0.94大于0.90，合适。

再者平均残差协方差标准化的总和SRMR为0.0060，小于0.05，合适。

第四，比较适配指数CFI为0.96，比较接近1，相当合适。 第五、渐进残差均方和平方根RMSEA为0.020小于0.05非常好。 二.统合模型分析

由于模型过于复杂，导致lisel软件识别为矩阵不够正定。故将模型中的顾客满意度与物流服务成本之间的关系去掉，形成下图 模型：

物流组织网络化程度 物流服务成本 顾客满意度 顾客满意度 生成图为：

1、估计（estimate）路径图

2、标准路径图（采用固定因子载荷法，只是在标准路径中体现不出） wc1 wc2 wc13 WF 0.98 0.98 0.99 WC 0.87 GZ -0.99 -0.13 0.99 0.98 0.98 0.91 1.00 wf1 0.17 0.03 wf2 gz1 gz2 gz3 0.00 0.04 0.03 0.03 0.04 0.05 GM 0.98 0.98 0.98 gm1 0.04 gm2 gm3 0.04 0.03 3、T检验的路径图

但是在T检验中发现WF作为WC与GZ的中介变量并不显著。但是为了作为练习就假设它是显著的。

点击“output中的indices”得到下面文字：

Degrees of Freedom = 40

Minimum Fit Function Chi-Square = 228.80 (P = 0.0) Normal Theory Weighted Least Squares Chi-Square = 220.04 (P = 0.0) Estimated Non-centrality Parameter (NCP) = 180.04 90 Percent Confidence Interval for NCP = (137.21 ; 230.38) Minimum Fit Function Value = 1.65 Population Discrepancy Function Value (F0) = 1.30 90 Percent Confidence Interval for F0 = (0.99 ; 1.66) Root Mean Square Error of Approximation (RMSEA) = 0.18 90 Percent Confidence Interval for RMSEA = (0.16 ; 0.20) P-Value for Test of Close Fit (RMSEA < 0.05) = 0.00 Expected Cross-Validation Index (ECVI) = 1.96 90 Percent Confidence Interval for ECVI = (1.65 ; 2.32) ECVI for Saturated Model = 0.95 ECVI for Independence Model = 31.50

Chi-Square for Independence Model with 55 Degrees of Freedom = 4356.65 Independence AIC = 4378.65

Model AIC = 272.04 Saturated AIC = 132.00 Independence CAIC = 4422.01 Model CAIC = 374.52 Saturated CAIC = 392.15 Normed Fit Index (NFI) = 0.95 Non-Normed Fit Index (NNFI) = 0.94 Parsimony Normed Fit Index (PNFI) = 0.69 Comparative Fit Index (CFI) = 0.96 Incremental Fit Index (IFI) = 0.96 Relative Fit Index (RFI) = 0.93 Critical N (CN) = 39.69 Root Mean Square Residual (RMR) = 0.020 Standardized RMR = 0.0063 Goodness of Fit Index (GFI) = 0.78 Adjusted Goodness of Fit Index (AGFI) = 0.63 Parsimony Goodness of Fit Index (PGFI) = 0.47

1. 计算卡方自由度比为=228.8/40=5.72，模型适配不佳，对真实数据反映不足，这主要是由于数据是随机生成并被操纵。 2. 其次非规准适配指标NNFI为0.94大于0.90，合适。

3. 再者平均残差协方差标准化的总和SRMR为0.0063，小于0.05，合适。

4. 第四，比较适配指数CFI为0.96，比较接近1，相当合适。 5. 第五、渐进残差均方和平方根RMSEA为0.18大于1，不是很适配。 第三、中介效应分析：

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