SPSS AMOS For the Structural Equation Modeling SPSS AMOS For the Structural Equation Modeling

SPSS AMOS For the Structural Equation Modeling Figure 58: 47

SPSS AMOS For the Structural Equation Modeling Figure 59: ii. The Model Fit: (Figure 60) The measures are classed into three general groups: absolute measures, incremental measures, and parsimony fit measure, and basic elements underlying all GOF measures. The measures are classed into three general groups: absolute measures, incremental measures, and parsimony fit measure, and basic elements underlying all the Goodness-of-Fit (GOF) measures. According to Hair Jr, et al., [9] - The basis of Goodness-of-Fit (GOF): Chi-square (X2) GOF Degrees of Freedom (DF) Statistical Significance of X2 (P-value =0.000) 48

SPSS AMOS For the Structural Equation Modeling Figure 60: - Absolute Fit Indices: Goodness-of-Fit Index(GFI): range 0-1 (>0.9good) Root Mean Square of Approximation (RMSEA): range 0.03-0.08 Root Mean Square Residual (RMR) and Standardized Root Mean Residual (SRMR) Normed Chi-Square (Chi-Square/ DF) Other absolutes indices - Incremental Fit Indices 49

SPSS AMOS For the Structural Equation Modeling Normed Fit Index (NFI): range 0-1 Tucker Lewis Index (TLI): Comparative Fit Index (CFI): Range 0-1 (>0.90 good) Relative Noncentrality Index (RNI): Range 0-1 - Parsimony Fit Indices Adjusted Goodness of Fit Index (AGFI) Parsimony Normed Fit Index (PNFI) iii. The Modification Indices: (Figure 61) Figure 61: 50

SPSS AMOS For the Structural Equation Modeling We can increase indexes to model fit by draw covariances correlating the residuals of the observed indicator represented by a double-headed arrow. We will identify the largest MI and also examine the MIs for the factor loadings. For this exercise draw covariance e21<> e22. Then re-run and check our indexes, and continue to draw covariance from the highest MI between the other error pairs until the goondness of fit model. In addition, we can cut several items to increase the goodness- of- fit model. However, we should consider deleting the items when it is essential because we may have a deleted item that has measurement value. Therefore, if it is necessary to delete items to get a good application model, then we should delete the item with the smallest loading value, sometimes we delete the item has the higher loading but it has effects to increase goodness-of-fit model that do not lose questions needs measure. So, it depends on our experience and on whether the item has a measurable value or not that we consider to delete it. For this exercise, we consider deleting SQ20 item and drawing covariance between the residuals of the observed indicator related. Finally, we see this shape. From the toolbar select Save As, we save the CFA file. We look like this (Figure 62) Figure 62: 51

SPSS AMOS For the Structural Equation Modeling The View Text of CFA Estimates (Group number 1 - Default model) Scalar Estimates (Group number 1 - Default model) Maximum Likelihood Estimates Regression Weights: (Group number 1 - Default model) (Table 0.23) Standardized Regression Weights: (Group number 1 - Default model) (Table 0.24) Covariances: (Group number 1 - Default model) (Table 0.25) Correlations: (Group number 1 - Default model) (Table 0.26) Variances: (Group number 1 - Default model) (Table 0.27) Squared Multiple Correlations: (Group number 1 - Default model) (Table 0.28 Matrices (Group number 1 - Default model) Residual Covariances (Group number 1 - Default model) (Table 0.29) Standardized Residual Covariances (Group number 1 - Default model) (Tables 0.30-0.43) Modification indices Covariances: (Group number 1 - Default model) (Table 0.44) Model fit Model Fit Summary CMIN (Table 0.45) RMR, GFI (Table 0.46) Baseline Comparisons (Table 0.47) Parsimony-Adjusted Measures (Table 0.48) NCP (Table 0.49) FMIN (Table 0.50) RMSEA (Table 0.51) AIC (Table 0.52) ECVI (Table 0.53) HOELTER (Tables 0.54,0.55) Table 0.23: Regression Weights: (Group number 1 - Default model) Estimate S.E. C.R. P Label WOM24 <--- WOM 1.032 0.047 22.043 *** WOM23 <--- WOM 0.99 0.044 22.692 *** WOM22 <--- WOM 0.99 0.043 23.049 *** WOM21 <--- WOM 1 RI27 <--- RI 1.2 0.062 19.282 *** RI26 <--- RI 1.114 0.077 14.532 *** RI25 <--- RI 1 RM2 <--- RM 1.137 0.079 14.423 *** RM3 <--- RM 1.118 0.083 13.434 *** RM4 <--- RM 1.125 0.069 16.305 *** RM6 <--- RM 1.138 0.079 14.372 *** RM7 <--- RM 1.214 0.083 14.707 *** RM8 <--- RM 1.165 0.082 14.24 *** 52

SPSS AMOS For the Structural Equation Modeling RM9 <--- RM 1.073 0.078 13.821 *** RM10 <--- RM 1.071 0.077 13.827 *** RM1 <--- RM 1 RM11 <--- RM 1.055 0.081 13.089 *** SQ14 <--- SQ 1.087 0.07 15.543 *** SQ15 <--- SQ 1.06 0.071 14.875 *** SQ16 <--- SQ 0.895 0.057 15.807 *** SQ17 <--- SQ 0.966 0.064 15.01 *** SQ18 <--- SQ 1.053 0.072 14.716 *** SQ19 <--- SQ 1.006 0.068 14.785 *** SQ12 <--- SQ 1 SQ13 <--- SQ 1.161 0.073 15.859 *** Table 0.24: Estimate WOM24 <--- WOM 0.859 WOM23 <--- WOM 0.872 WOM22 <--- WOM 0.879 WOM21 <--- WOM 0.874 RI27 <--- RI 0.857 RI26 <--- RI 0.701 RI25 <--- RI 0.829 RM2 <--- RM 0.802 RM3 <--- RM 0.747 RM4 <--- RM 0.77 RM6 <--- RM 0.8 RM7 <--- RM 0.819 RM8 <--- RM 0.791 RM9 <--- RM 0.768 RM10 <--- RM 0.769 RM1 <--- RM 0.706 RM11 <--- RM 0.727 SQ14 <--- SQ 0.823 SQ15 <--- SQ 0.79 SQ16 <--- SQ 0.691 SQ17 <--- SQ 0.796 SQ18 <--- SQ 0.781 SQ19 <--- SQ 0.784 SQ12 <--- SQ 0.732 SQ13 <--- SQ 0.837 Table 0.25: Estimate S.E. C.R. P Label RM <--> SQ 0.387 0.043 8.982 *** WOM <--> RM 0.372 0.04 9.264 *** RI <--> RM 0.344 0.038 9.127 *** WOM <--> SQ 0.45 0.047 9.668 *** RI <--> SQ 0.403 0.043 9.379 *** WOM <--> RI 0.479 0.044 10.766 *** e21 <--> e22 0.112 0.019 5.875 *** e18 <--> e19 0.123 0.02 6.112 *** 53

SPSS AMOS For the Structural Equation Modeling e3 <--> e4 0.093 0.021 4.497 *** e24 <--> e26 0.103 0.021 4.861 *** e1 <--> e5 0.148 0.026 5.789 *** e17 <--> e20 0.099 0.021 4.713 *** e5 <-->e6 0.094 0.02 4.701 *** e19 <--> e20 0.084 0.018 4.523 *** Table 0.26: Estimate RM <--> SQ 0.879 WOM <--> RM 0.794 RI <--> RM 0.817 WOM <--> SQ 0.837 RI <--> SQ 0.833 WOM <--> RI 0.931 e21 <--> e22 0.404 e18 <--> e19 0.383 e3 <--> e4 0.298 e24 <--> e26 0.304 e1 <--> e5 0.337 e17 <--> e20 0.278 e5 <--> e6 0.269 e19 <--> e20 0.236 Table 0.27: Estimate S.E. C.R. P Label WOM 0.571 0.056 10.271 *** RI 0.463 0.05 9.287 *** RM 0.383 0.051 7.453 *** SQ 0.507 0.065 7.844 *** e1 0.438 0.036 12.243 *** e2 0.291 0.026 11.18 *** e3 0.286 0.025 11.298 *** e4 0.342 0.029 11.659 *** e5 0.444 0.035 12.551 *** e6 0.274 0.023 11.733 *** e7 0.359 0.03 11.884 *** e8 0.176 0.017 10.631 *** e9 0.165 0.016 10.518 *** e10 0.177 0.017 10.698 *** e11 0.216 0.02 10.988 *** e12 0.211 0.02 10.435 *** e13 0.594 0.049 12.088 *** e14 0.241 0.025 9.582 *** e15 0.32 0.027 11.852 *** e17 0.385 0.031 12.324 *** e18 0.274 0.024 11.609 *** e19 0.379 0.031 12.219 *** e20 0.332 0.028 12.008 *** e21 0.278 0.024 11.54 *** e22 0.277 0.024 11.324 *** 54

SPSS AMOS For the Structural Equation Modeling e23 0.31 0.026 11.747 *** e24 0.38 0.031 12.189 *** e25 0.307 0.026 11.968 *** e26 0.304 0.026 11.91 *** Table 0.28: Estimate RM10 0.591 RM9 0.589 RM11 0.529 RM8 0.626 RM7 0.671 RM6 0.64 RM4 0.593 RM3 0.558 RM2 0.644 RM1 0.499 SQ19 0.615 SQ18 0.61 SQ17 0.633 SQ16 0.478 SQ15 0.625 SQ14 0.677 SQ13 0.701 SQ12 0.536 RI25 0.687 RI26 0.492 RI27 0.734 WOM21 0.765 WOM22 0.772 WOM23 0.76 WOM24 0.738 Table 0.29: Residual Covariances (Group number 1 - Default model) RM 10 RM 9 RM 11 RM 8 RM 7 RM 6 RM 4 RM 3 RM 2 RM 1 SQ 19 SQ 18 SQ 17 SQ 16 SQ 15 SQ 14 SQ 13 SQ 12 RI 25 RI 26 R I 2 7 W O M 2 1 W O M 2 2 W O M 2 3 W O M 2 4 RM 10 0 RM 9 0.0 44 0 RM 11 0 0.0 21 0 RM 8 -0.0 29 -0.0 03 0.0 15 0 RM 7 0.0 22 -0.02 0.0 24 0.0 54 0 RM 6 0.0 07 0.01 0.01 0.0 37 0 0 RM 4 0.0 08 -0.0 02 -0.0 09 0 -0.0 65 -0.06 0.0 01 55

SPSS AMOS For the Structural Equation Modeling RM 3 -0.01 -0.0 23 -0.04 -0.0 12 -0.02 -0.0 09 0.0 41 0.0 15 RM 2 -0.0 28 -0.0 36 -0.0 41 -0.0 46 -0.0 07 0.0 18 0.0 85 0.0 17 0 RM 1 -0.0 43 0.0 3 -0.0 51 -0.0 33 -0.0 34 0.0 21 0.0 03 0.0 53 0.0 93 0 SQ 19 0.0 28 -0.0 22 0.0 09 -0.0 09 0.0 26 -0.0 04 -0.0 17 0.0 03 -0.0 17 -0.0 36 0 SQ 18 0.0 33 -0.0 32 0.0 09 -0.0 21 0.0 07 -0.0 28 0.0 01 0.0 38 -0.0 23 -0.0 32 0.0 33 0 SQ 17 -0.0 23 0.0 25 -0.0 07 -0.0 02 0.0 3 -0.0 04 -0.0 31 -0.0 13 0.0 34 0.0 37 0.0 49 0.0 2 0 SQ 16 0.0 24 0.0 24 0.0 62 0.0 68 0.0 61 0.0 11 0.0 11 -0.0 16 0.0 17 0.0 46 -0.0 29 -0.0 25 -0.0 13 -0.0 09 SQ 15 0 -0.0 42 0.0 01 -0.0 01 -0.0 06 -0.0 56 0 -0.0 23 -0.0 15 -0.0 49 0.0 39 0.0 18 -0.0 48 -0.0 29 0 SQ 14 0.0 3 -0.0 17 0.0 11 -0.0 14 0.0 12 -0.0 46 0.0 29 0.0 56 -0.0 11 -0.0 36 0.0 05 0.0 33 -0.0 37 -0.0 4 0 0 SQ 13 -0.0 22 0.0 09 -0.0 16 0.0 4 -0.0 12 -0.0 35 0.0 13 0.0 23 0.0 22 0.0 29 -0.0 5 -0.0 3 0.0 03 0.0 38 -0.0 03 0.0 32 0 SQ 12 -0.0 28 0.0 25 0.0 62 0.0 48 -0.0 02 -0.0 07 0 -0.0 19 -0.0 14 0.0 32 -0.0 71 -0.0 38 -0.0 39 -0.0 13 0.0 2 0.0 04 0.0 68 0 RI 25 0.0 06 0.0 46 0.0 68 -0.0 21 -0.0 16 -0.0 07 0.0 12 -0.0 15 -0.0 01 0.0 01 -0.0 39 -0.0 61 -0.0 13 0.0 52 0.0 09 -0.0 69 -0.0 3 0.0 89 0 RI 26 0.0 15 -0.0 38 0.0 01 0.0 07 0.0 24 -0.0 17 -0.0 06 0.0 16 0.0 64 -0.0 52 0.1 12 0.0 04 0.0 33 -0.0 75 0.0 4 0.0 69 0.0 38 -0.0 84 -0.0 39 0 RI 27 0.0 05 -0.0 28 -0.0 1 0.0 2 0.0 02 -0.0 1 0.0 01 -0.0 18 0.0 03 -0.0 73 0 0.0 07 -0.0 22 0.0 15 0.0 32 -0.0 09 0.0 19 0.0 32 -0.0 14 0.0 72 0 WO M 21 -0.0 12 0.0 33 0.0 14 0.0 2 -0.0 15 -0.0 11 0.0 02 -0.0 17 -0.0 17 0.0 05 0.0 03 -0.0 05 0.0 03 0.0 56 0.0 07 -0.0 28 -0.0 23 0.0 88 0.0 79 -0.0 27 -0. 0 2 7 0 WO M 22 -0.0 02 0.0 12 -0.0 46 -0.0 3 -0.0 07 -0.0 25 -0.0 04 -0.0 01 0.0 1 -0.0 15 -0.0 29 -0.0 3 0.0 39 0.0 28 -0.0 08 -0.0 26 -0.0 15 -0.0 11 -0.0 12 -0.0 53 -0. 0 2 2 0. 0 0 3 0 WO M 23 0.0 51 -0.0 08 0.0 75 0.0 04 0.0 17 0.0 1 0.0 29 0.0 26 0.0 28 -0.0 26 0.0 45 0.0 25 0.0 37 0.0 19 0.0 3 -0.0 08 -0.0 14 0.0 18 0.0 2 0.0 06 0. 0 1 4 -0. 0 1 2 -0. 0 0 4 0 WO M 24 0.0 05 0.0 25 -0.0 26 -0.0 07 -0.0 35 -0.0 52 0.0 12 0.0 17 -0.0 13 -0.0 46 0.0 09 -0.0 44 0.0 15 0.0 46 0.0 21 -0.0 41 -0.0 23 -0.0 27 -0.0 06 -0.0 54 0. 0 0 4 -0. 0 0 8 0. 0 3 1 -0. 0 1 0 Table 0.30: Standardized Residual Covariances (Group number 1 - Default model) RM 10 RM 9 RM 11 RM 8 RM 7 RM 6 RM 4 RM 3 RM 2 RM 1 SQ 19 SQ 18 SQ 17 SQ 16 SQ 15 SQ 14 SQ 13 SQ 12 RI 25 RI 26 RI 27 WO M 21 WO M 22 W O M 2 3 W O M 2 4 RM 10 0 RM 9 0.9 45 0 RM 11 0 0.4 43 0 RM 8 -0.5 89 -0.0 69 0.3 01 0 RM 7 0.4 49 -0.4 03 0.4 65 1.0 1 0 56

SPSS AMOS For the Structural Equation Modeling RM 6 0.1 49 0.2 03 0.2 1 0.7 27 0 0 RM 4 0.1 74 -0.0 48 -0.1 88 0 -1.2 43 -1.2 13 0.0 24 RM 3 -0.1 98 -0.4 77 -0.7 85 -0.2 32 -0.3 73 -0.1 85 0.7 64 0.2 31 RM 2 -0.5 94 -0.7 68 -0.8 54 -0.9 21 -0.1 28 0.3 77 1.7 13 0.3 07 0 RM 1 -0.9 46 0.6 63 -1.0 88 -0.6 74 -0.6 87 0.4 43 0.0 55 1.0 82 1.9 85 0 SQ 19 0.5 99 -0.4 68 0.1 83 -0.1 85 0.5 17 -0.0 74 -0.3 47 0.0 5 -0.3 44 -0.7 53 0 SQ 18 0.6 52 -0.6 48 0.1 82 -0.3 87 0.1 25 -0.5 39 0.0 13 0.7 13 -0.4 56 -0.6 52 0.6 11 0 SQ 17 -0.5 13 0.5 6 -0.1 45 -0.0 39 0.6 12 -0.0 88 -0.6 54 -0.2 78 0.7 31 0.8 27 0.9 8 0.3 79 0 SQ 16 0.5 23 0.5 03 1.2 86 1.3 61 1.2 16 0.2 31 0.2 2 -0.3 13 0.3 46 0.9 71 -0.5 71 -0.4 58 -0.2 6 -0.1 42 SQ 15 -0.0 04 -0.8 36 0.0 1 -0.0 11 -0.1 05 -1.0 97 0.0 09 -0.4 34 -0.2 89 -0.9 85 0.7 09 0.3 07 -0.9 19 -0.5 47 0 SQ 14 0.6 13 -0.3 38 0.2 21 -0.2 75 0.2 27 -0.8 98 0.5 54 1.0 58 -0.2 22 -0.7 22 0.0 98 0.5 78 -0.7 14 -0.7 51 0 0 SQ 13 -0.4 29 0.1 76 -0.2 93 0.7 15 -0.2 14 -0.6 56 0.2 31 0.4 08 0.4 06 0.5 56 -0.8 68 -0.4 96 0.0 58 0.6 72 -0.0 42 0.5 29 0 SQ 12 -0.5 64 0.5 08 1.1 98 0.8 99 -0.0 42 -0.1 41 -0.0 07 -0.3 64 -0.2 65 0.6 46 -1.3 -0.6 58 -0.7 57 -0.2 33 0.3 48 0.0 77 1.1 4 0 RI 25 0.1 48 1.0 79 1.5 59 -0.4 72 -0.3 45 -0.1 63 0.2 6 -0.3 34 -0.0 21 0.0 35 -0.8 68 -1.2 81 -0.2 9 1.1 68 0.1 9 -1.4 48 -0.6 03 1.8 76 0 RI 26 0.2 69 -0.7 02 0.0 17 0.1 19 0.4 03 -0.3 03 -0.1 13 0.2 83 1.1 43 -0.9 49 1.9 45 0.0 73 0.6 08 -1.3 07 0.6 56 1.1 44 0.5 98 -1.3 72 -0.7 11 0 RI 27 0.1 07 -0.5 68 -0.1 96 0.3 8 0.0 43 -0.1 93 0.0 14 -0.3 34 0.0 51 -1.4 76 0.0 06 0.1 33 -0.4 43 0.2 96 0.5 79 -0.1 54 0.3 29 0.5 77 -0.2 68 1.1 19 0 WO M 21 -0.2 72 0.7 3 0.2 95 0.4 11 -0.3 02 -0.2 28 0.0 39 -0.3 48 -0.3 65 0.1 01 0.0 66 -0.0 91 0.0 62 1.1 86 0.1 33 -0.5 48 -0.4 4 1.7 27 1.7 43 -0.4 66 -0.4 96 0 WO M 22 -0.0 53 0.2 73 -1.0 01 -0.6 37 -0.1 5 -0.5 56 -0.0 78 -0.0 32 0.2 26 -0.3 31 -0.6 08 -0.5 94 0.8 62 0.6 02 -0.1 65 -0.5 12 -0.2 86 -0.2 24 -0.2 65 -0.9 32 -0.4 1 0.0 58 0 WO M 23 1.1 52 -0.1 69 1.6 27 0.0 75 0.3 52 0.2 28 0.6 26 0.5 39 0.6 17 -0.5 89 0.9 36 0.4 98 0.8 19 0.3 97 0.6 -0.1 67 -0.2 61 0.3 63 0.4 52 0.1 07 0.2 7 -0.2 42 -0.0 84 0 WO M 24 0.0 97 0.5 22 -0.5 26 -0.1 4 -0.6 89 -1.0 74 0.2 35 0.3 39 -0.2 7 -0.9 7 0.1 8 -0.8 29 0.3 15 0.9 2 0.3 99 -0.7 66 -0.4 18 -0.5 05 -0.1 35 -0.9 06 0.0 71 -0.1 48 0.6 09 -0. 1 8 9 0 Table 0.31: M.I. Par Change e12 <--> e1 22.044 0.081 e8 <--> e1 20.959 0.072 e8 <--> e12 52.804 0.091 M.I. Par Change M.I. Par Change Table 0.32: Iteration Negative Condition # Smallest Diameter F NTries Ratio 0 e 18 -2.006 9999 7709.233 0 9999 1 e* 23 -0.521 2.751 4779.52 19 0.35 57

SPSS AMOS For the Structural Equation Modeling 2 e 14 -0.516 1.228 3111.234 5 0.987 3 e 7 -0.572 0.745 2304.001 4 0.837 4 e* 3 -0.166 0.457 1785.383 4 0.91 5 e 0 571.111 0.75 1173.143 5 0.864 6 e 0 340.577 0.63 989.748 3 0 7 e 0 697.128 0.777 883.145 1 0.96 8 e 0 925.114 0.312 868.382 1 1.098 9 e 0 1088.865 0.114 867.672 1 1.053 10 e 0 1084.515 0.012 867.665 1 1.009 11 e 0 1126.218 0 867.665 1 1 Table 0.33: Model NPAR CMIN DF P CMIN/DF Default model 64 867.665 261 0 3.324 Saturated model 325 0 0 Independence model 25 7981.831 300 0 26.606 Table 0.34: Model RMR GFI AGFI PGFI Default model 0.031 0.836 0.795 0.671 Saturated model 0 1 Independence model 0.451 0.115 0.041 0.106 Table 0.35: Model NFI Delta1 RFI rho1 IFI Delta2 TLI rho2 CFI Default model 0.891 0.875 0.921 0.909 0.921 Saturated model 1.000 1.000 1.000 Independence model .000 .000 .000 .000 .000 Table 0.36: Model PRATIO PNFI PCFI Default model 0.870 0.775 0.801 Saturated model .000 .000 .000 Independence model 1.00 .000 .000 Table 0.37: Model NCP LO 90 HI 90 Default model 606.665 521.106 699.814 Saturated model .000 .000 .000 Independence model 7681.831 7393.89 7976.133 Table 0.38: Model FMIN F0 LO 90 HI 90 Default model 2.465 1.723 1.48 1.988 Saturated model .000 .000 .000 .000 Independence model 22.676 21.823 21.005 22.659 58

SPSS AMOS For the Structural Equation Modeling Table 0.39: Model RMSEA LO 90 HI 90 PCLOSE Default model 0.081 0.075 0.087 .000 Independence model 0.270 0.265 0.275 .000 Table 0.40: Model AIC BCC BIC CAIC Default model 995.665 1005.873 1243.119 1307.119 Saturated model 650.000 701.84 1906.602 2231.602 Independence model 8031.831 8035.819 8128.493 8153.493 Table 0.41: Model ECVI LO 90 HI 90 MECVI Default model 2.829 2.586 3.093 2.858 Saturated model 1.847 1.847 1.847 1.994 Independence model 22.818 22.000 23.654 22.829 Table 0.42: Model HOELTER 0.05 HOELTER 0.01 Default model 122 129 Independence model 16 16 Table 0.43: Minimization: 0.051 Miscellaneous: 0.744 Bootstrap: .000 Total: 0.795 Table 0.44: M.I. Par Change e12 <--> e1 22.044 0.081 e8 <--> e1 20.959 0.072 e8 <--> e12 52.804 0.091 Table 0.45: Model NPAR CMIN DF P CMIN/DF Default model 64 867.665 261 .000 3.324 Saturated model 325 .000 0 Independence model 25 7981.831 300 .000 26.606 Table 0.46: Model RMR GFI AGFI PGFI Default model 0.031 0.836 0.795 0.671 Saturated model .000 1.000 Independence model 0.451 0.115 0.041 0.106 Table 0.47: Model NFI RFI IFI TLI CFI Delta1 rho1 Delta2 rho2 Default model 0.891 0.875 0.921 0.909 0.921 Saturated model 1.000 1.000 1.000 Independence model .000 .000 .000 .000 .000 59

SPSS AMOS For the Structural Equation Modeling Table 0.48: Model PRATIO PNFI PCFI Default model 0.870 .775 0.801 Saturated model .000 .000 .000 Independence model 1.000 .000 .000 Table 0.49: Model NCP LO 90 HI 90 Default model 606.665 521.106 699.814 Saturated model .000 .000 .000 Independence model 7681.831 7393.890 7976.133 Table 0.50: Model FMIN F0 LO 90 HI 90 Default model 2.465 1.723 1.48 1.988 Saturated model .000 .000 .000 .000 Independence model 22.676 21.823 21.005 22.659 Table 0.51: Model RMSEA LO 90 HI 90 PCLOSE Default model .081 0.075 0.087 .000 Independence model 0.270 0.265 0.275 .000 Table 0.52: Model AIC BCC BIC CAIC Default model 995.665 1005.873 1243.119 1307.119 Saturated model 650.000 701.840 1906.602 2231.602 Independence model 8031.831 8035.819 8128.493 8153.493 Table 0.53: Model ECVI LO 90 HI 90 MECVI Default model 2.829 2.586 3.093 2.858 Saturated model 1.847 1.847 1.847 1.994 Independence model 22.818 22.000 23.654 22.829 Table 0.54: Model HOELTER 0.05 HOELTER 0.01 Default model 122 129 Independence model 16 16 Table 0.55: Minimization: .051 Miscellaneous: .744 Bootstrap: .000 Total: .795 Interpret Confirmatory Factor Analysis (CFA) and Model Goodness-of-Fit The SEM was conducted using CFA, in which each variable was examined by CFA to assess the construct and the correct assignment of variables. The study model was assessed in terms of standardized regression weights, Modification Indices (MIs), and standardized residuals for pairs of items [9]. The results are shown in Table 3. The standardized coefficients of all items ranged from 0.70 to 0.86, thus exceeding the required threshold of 0.5. The Average Variance Extracted (AVE) values were between 0.60 and 0.76, thus exceeding the cut60

SPSS AMOS For the Structural Equation Modeling off of 0.50 and thereby indicating that a large proportion of the variance was explained by our constructs (Table 3). Table 3: Confirmatory factor analysis results and Model goodness-of-fit. Construct measures Standardized coefficients Average variance extracted (AVE) Composite reliability (CR) Relationship Marketing (RM) 0.594 0.936 RM1<---RM 0.706 RM2<---RM 0.802 RM3<---RM 0.747 RM4<---RM 0.77 RM6<---RM 0.8 RM7<---RM 0.819 RM8<---RM 0.791 RM10<---RM 0.769 RM11<---RM 0.727 RM9<---RM 0.768 Word of Mouth (WOM) 0.759 0.926 WOM21<---WOM 0.874 WOM22<---WOM 0.879 WOM23<---WOM 0.872 WOM24<---WOM 0.859 Service Quality (SQ) 0.629 0.922 SQ12<---SQ 0.732 SQ13<---SQ 0.837 SQ14<---SQ 0.825 SQ15<---SQ 0.792 SQ17<---SQ 0.795 SQ18<---SQ 0.782 SQ19<---SQ 0.785 Repurchase Intention (RI) 0.637 0.839 RI25<---RI 0.828 RI26<---RI 0.701 RI27<---RI 0.857 Chi-square=823.995; df=240; Chi-square/df=3.433; P=0.000; GFI=0.84; NFI=0.89; TLI=0.91; CFI=0.92; RMSEA=0.08; AGFI=0.80 The standardized coefficients of all items ranged from 0.70 to 0.86, thus exceeding the required threshold of 0.5. The average variance extracted (AVE) values were between 0.60 and 0.76, thus exceeding the cut-off of 0.50 and thereby indicating that a large proportion of the variance was explained by our constructs (Table 3). The AVEs exceeded the squared correlations between any pair of constructs, suggesting high discriminant validity. The composite reliability (CR) values for all constructs were between 0.84 and 0.94, thus exceeding the cut-off value of 0.70 for adequate internal consistency [9]. Especially, the ratio of 2 χ to the degrees of freedom was 3.433 (P=0.000), which the 2 χ test was known to be sensitive to sample size, and several widely used goodness-of-fit (GFI) indices showed that the confirmatory factor model was a good fit to the data. The GFI = 0.84 [cut-off = 0.80, adjusted GFI = 0.84, normalized fit index [NFI] = 0.89 [requirement = value of 0-1], root mean squared error of approximation (RMSEA) = 0.08 [requirement = value from 0.05-0.08), comparative fit index [CFI] = 0.92, Tucker-Lewis index [TLI] = 0.91 [cut-off = 0.9]) [9]. Thus, the overall model fit was satisfactory, and all scales meet the reliability and validity requirements. The Structural Model in SEM The structural model indicates the causal and correlational links among latent variables in a theoretical model. We use results of the above CFA, select cut icon to cut off the covariance with the double- head arrows between the latent variables. It sees like this (Figures 63,64). 61

SPSS AMOS For the Structural Equation Modeling Figure 63: Figure 64: Select the Move objects icon combines with the Preserve Symmetries to move all the shape. Select the Rotate the indicators of the latent variable icon. We draw the covariance to correlate between the latent variables by the single-headed arrow connecting the latent variables follow the causality of hypothesis proposal model. Then, we create a residual for the latent variable, use the Add Unique Variable 62

SPSS AMOS For the Structural Equation Modeling icon. From the toolbar select Plugins> the Name Unobserved Variable like this shape. Finally, we see like this. From the toolbar select the File> Save As, we save the Structural model file, it looks this (Figure 65) Figure 65: The output view text it looks like (Figure 66) The View text also has three of the paths similar to the measurement model (CFA), including the Estimates, the Modification Indices, and the Model Fit. As results of this structural model, we consider the hypothesis and the path in the Regression Weights table and the Standardized Regression Weights. The structural model output also displays the unstandardized and standardized regression coefficients. The unstandardized coefficients and associated test statistics appear below. Each unstandardized regression coefficient represents the amount of change in the dependent or mediating variable for each unit change in the variable predicting it such as the figure has shown that WOM increases 0.316 for each 1.000 increase in RM. (Figure 67) This figure displays the unstandardized estimate, its standard error (abbreviated S.E.), and the estimate divided by the standard error (abbreviated C.R. for Critical Ratio). The probability value associated with the null hypothesis that the test is zero is displayed under the P column. The hypotheses were evaluated by standardized coefficients and path coefficients with significance (sig<0.05). The symbol*** (sig= 0.001). 63

SPSS AMOS For the Structural Equation Modeling Figure 66: 64

SPSS AMOS For the Structural Equation Modeling Figure 67: Standardized estimates allow us to evaluate the relative contributions of each predictor variable to each outcome variable. The standardized estimates for the research model appear below. (Figure 68) Once we have obtained a model that fits well and that is theoretically consistent and it provides statistically significant parameter estimates, we must interpret it in the light of our research questions and then distill our results in written form for publication. It is important to note that even though this model fits the data well and provides a theoretically consistent set of findings, there may be other equivalent models that fit the data equally well. There may also be non-equivalent alternative models that fit the data better than this model. Researchers should strive to test and rule out likely alternative models whenever possible. In this exercise, we present the hypotheses follow as: 65

SPSS AMOS For the Structural Equation Modeling Figure 68: Hypotheses Testing Hypotheses Testing The hypotheses of the study present in Table 4. Hypothesis H1: RM has a positive influence on WOM Hypothesis H1 is represented by the coefficient of the path RM--->WOM, at the statistically significant of 0.269 (p = 0.004), which indicates that this hypothesis was not supported. Hypothesis H2: RM has a positive influence on RI Hypothesis H2 descriptive by the coefficient of the path (RM---> RI) of statistically significant (0.178; p = 0.034), showing that this hypothesis was not supported. Hypothesis H3: RM influence on SQ 66

SPSS AMOS For the Structural Equation Modeling Table 4: Hypothesis test results. Hypothesis Path Standardized coefficients Sig. Results H1 RM--->WOM 0.269 0.004 Rejected H2 RM--->RI 0.178 0.034 Rejected H3 RM--->SQ 0.877 *** Accepted H4 SQ--->WOM 0.599 *** Accepted H5 SQ--->RI 0.056 0.56 Rejected H6 WOM--->RI 0.743 *** Accepted The probability of a t-value equal to or greater than the actual t-value is a two-tailed test for significance of the coefficient under the null hypothesis that the true value is zero. The symbol*** indicates that the null hypothesis is rejected at the 0.001 level of significance. This hypothesis is shown by the coefficient of the path RM-- >SQ. The structure model coefficient value of 0.877 was statistically significant (p = 0.001) and clearly indicates a positive influence of RM on SQ. Hypothesis H4: SQ influences on WOM Hypothesis H4 is presented by the coefficient of the path SQ--->WOM, in which the structures model coefficient of 0.599 had a positive sign and was statistically significant (p = 0.001). It indicates a positive effect of SQ on WOM. Hypothesis H5: SQ influence on RI The path coefficient (SQ---> RI) for H5 was statistically significant (0.056; p =0.560), showing that this hypothesis was not supported. Hypothesis H6: WOM has a positive effect on RI. The path coefficient (WOM--->RI) for H6 was statistically significant (0.743; p=0.001), showing that that WOM has a significant effect on RI. Biography Dr. Thi Le Ha Nguyen VNU University of Medicine and Pharmacy, Vietnam National University, Hanoi, Correspondence email: [email protected] Thi Le Ha Nguyen is currently working at VNU University of Medicine and Pharmacy, Vietnam National University, Hanoi, a lecturer of Public Health and preventive Department. She has been a Medical Doctor for 21 years in Vietnam. She was graduated from the Mahidol University, Thailand, and was awarded Master of Primary Healthcare Management. She holds a PhD on Healthcare Management from Graduated school of Medical Sciences, Kanazawa University, Japan. Prof. J. Paulo Moreira IHMRDC - International Healthcare Management Research & Development Centre Shandong Provincial Qianfoshan Hospital, Jinan, Shandong, China, 16766, jingshi road, Jinan, Shandong, China Correspondence email: [email protected] J. Paulo Moreira holds a PhD from the University of Manchester, United Kingdom in Healthcare Management and has over twenty years 67

SPSS AMOS For the Structural Equation Modeling experience in international healthcare management research and executive roles. Visiting Professor of Healthcare Management and Public Health in several universities around the World. Prof. Keisuke Nagase Graduated school of Medical Sciences, Kanazawa University, Kanazawa City, 920864, Japan Department of Corporate Planning, University Hospital, Kanazawa University, Kanazawa City, 9208641, Japan Correspondence email: [email protected] Conducting research and teaching in hospital management, medical IT (including AI application), and Pulmonary Medicine for 20 years in medical schools, Keisuke Nagase provide service as a deputy director of the university hospital for finance, budget and IT. Nhat Anh Nguyen Faculty of Information and Computer Science, Kanazawa Institute of Technology Correspondence email: [email protected] Nhat Anh Nguyen is the 2nd year student at the Faculty of Information and Computer Science at Kanazawa Institute of Technology, Japan. He was awarded outstanding in math at Tagami Gaoka high school. Currently, his research group was awarded computer science research awarded by the Rector of Kanazawa Institute of Technology, Japan. References 1. Hoyle R H (1995) Structural equation modeling: Concepts, issues, and applications 1-13. 2. Wright S (1918) On the nature of size factors. Genetics 3(4): 367-374. 3. Teo T, Khine MS (2009) Structural equation modeling in educational research: Concepts and applications 3-10. 4. Kenny DA (1979) Correlation and causality. 5. Joreskog K, Sorbom D (1993) LISREL 8: structural equation modeling with the SIMPLIS command language. 6. Bentler PM (2006) EQS 6 Structural equations program manual. Encino, CA: Multivariate Software. 7. Arbuckle JL (2013) Amos [Computer software]. Chicago, IL: SPSS. 8. Muthén LK, Muthén BO (2002). How to use a Monte Carlo study to decide on sample size and determine power. Structural Equation Modeling 9: 599- 620. 9. Hair JF, Black WC, Babin BJ, Anderson RE (2014) Multivariate data analysis. London, UK: Prentice Hall. 10. Arbuckle JL (2017) Amos [Computer software]. Chicago, IL: SPSS. 11. Wolf EJ, Harrington KM, Clark SL, Miller W (2013) Sample size requirements for structural equation models: an evaluation of power, bias, and solution propriety. Educ Psychol Meas 73(6): 913-934. 12. Ndubisi N O (2007) Relationship marketing and customer loyalty. Marketing Intelligence & Planning 25(1): 98-106. 13. Lee S, Kim E (2017) The effects of Korean medical service quality and satisfaction on revisitintention ofthe United Arab Emirates Government sponsored patients. Asian Nurs Res 11(2): 142-149. 14. Kitapci O, Akdogan C, Dortyol IT (2014) The impact of service quality dimensions on patient satisfaction, repurchase intentions and word-of-mouth communication in the public healthcare industry. Procedia Social and Behavioral Sciences 148: 161-169. 15. Al Fraihi KJ, Latif SA (2016) Evaluation of outpatient service quality in Eastern Saudi Arabia. Saudi Med J 37(4): 420-428. 16. Gu D, Yang X, Li X, Jain HK, Liang C (2018) Understanding the role of mobile internet- based health services on patient satisfaction and word-of-mouth. Int J Environ Res Public Health 15(9): 1972. 17. George D, Mallery P (2010) SPSS for windows step by step: a simple study guide and reference 17.0 update. Boston, MA: Allyn & Bacon. 68

SPSS AMOS For the Structural Equation Modeling

SPSS AMOS For the Structural Equation Modeling