<xml> </xml><![endif]--><!--[if gte mso 9]><xml> Normal 0 false false false EN-ID X-NONE X-NONE </xml><![endif]--><!--[if gte mso 9]><xml> </xml><![endif]--><!--[if gte mso 10]> <style> /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:8.0pt; mso-para-margin-left:0cm; line-height:107%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi; mso-fareast-language:EN-US;} </style> <![endif]--> Consistent Partial Least Squares (PLSc) is an improvement upon PLS and an alternative to Variance-Based Covariance Structural Equation Modeling (CB-SEM). PLSc can overcome the weaknesses of PLS, which often results in inconsistent estimates, as well as address the limitations of CB-SEM. PLSc can predict models with both reflective and formative indicator designs, small sample sizes (30- 100), assumption-free and distribution-free characteristics, thus not requiring multivariate normal distribution of data. Therefore, statistical significance testing is addressed using bootstrap methods. Multigroup SEM with the PLSc approach can be used to test whether the relationships between variables in the model are significant and whether there are differences in the relationships between variables in different population subgroups. A case study on factors influencing human life quality with 187 samples divided into two groups based on island differences was analyzed using the PLSc approach for parameter estimation and further Multigroup analysis to test for differences in variable relationships between different groups.In this study, data were obtained from the Indonesian Central Bureau of Statistics (BPS) website and analyzed using smartPLS software. The variables used in the case study consist of exogenous latent variables, namely successful of economic(?), intermediary latent variables such as poverty (?1), and endogenous latent variables, namely human life quality (?2). The results of the case study indicate that after model modification, all indicators are significant for their respective latent variables. The structural model shows that in each region, economic success has a negative and significant effect on poverty, economy success has a positive and significant effect on human life quality, and poverty has a negative and significant effect on human life quality. The saturated model fit values indicate that the model is able to explain the data variation well. Meanwhile, multigroup analysis shows that the differences in variable relationships between the Java and Sumatra island are not significant "> <xml> </xml><![endif]--><!--[if gte mso 9]><xml> Normal 0 false false false EN-ID X-NONE X-NONE </xml><![endif]--><!--[if gte mso 9]><xml> </xml><![endif]--><!--[if gte mso 10]> <style> /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:8.0pt; mso-para-margin-left:0cm; line-height:107%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi; mso-fareast-language:EN-US;} </style> <![endif]--> Consistent Partial Least Squares (PLSc) is an improvement upon PLS and an alternative to Variance-Based Covariance Structural Equation Modeling (CB-SEM). PLSc can overcome the weaknesses of PLS, which often results in inconsistent estimates, as well as address the limitations of CB-SEM. PLSc can predict models with both reflective and formative indicator designs, small sample sizes (30- 100), assumption-free and distribution-free characteristics, thus not requiring multivariate normal distribution of data. Therefore, statistical significance testing is addressed using bootstrap methods. Multigroup SEM with the PLSc approach can be used to test whether the relationships between variables in the model are significant and whether there are differences in the relationships between variables in different population subgroups. A case study on factors influencing human life quality with 187 samples divided into two groups based on island differences was analyzed using the PLSc approach for parameter estimation and further Multigroup analysis to test for differences in variable relationships between different groups.In this study, data were obtained from the Indonesian Central Bureau of Statistics (BPS) website and analyzed using smartPLS software. The variables used in the case study consist of exogenous latent variables, namely successful of economic(?), intermediary latent variables such as poverty (?1), and endogenous latent variables, namely human life quality (?2). The results of the case study indicate that after model modification, all indicators are significant for their respective latent variables. The structural model shows that in each region, economic success has a negative and significant effect on poverty, economy success has a positive and significant effect on human life quality, and poverty has a negative and significant effect on human life quality. The saturated model fit values indicate that the model is able to explain the data variation well. Meanwhile, multigroup analysis shows that the differences in variable relationships between the Java and Sumatra island are not significant ">
Multigroup Structural Equation Modelling dengan Pendekatan Consistent Partial Least Square
Ni Kadek Sudarti, Dr. Abdurakhman, M.Si
2024 | Tesis | S2 Matematika
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