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<![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
">
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mso-bidi-theme-font:minor-bidi;
mso-fareast-language:EN-US;}
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<![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
">
