## Materials and Methods
### Structural Equation Model
#### 2.2.2 Structural Equation Model  
The structural equation model is a statistical multivariate model that can confirm the direct and indirect effect of independent variables on the dependent, and the degree of the causal relationship between them, related to a specific phenomenon [37; 38; 39]. [PERSON] (1934) [40] introduced this methodology to the field of natural science to the biological population. Since then, the scope of applications has greatly expanded and has been applied to social sciences, psychology, chemistry, and biology. After setting up the initial model reflecting the hypothesis, regression analysis, correlation analysis, and factor analysis were used to confirm the causal relationship. Particularly, factor analysis should be used to investigate the causal structure of hidden factors and complete the structure. This is due to the difference from the path model, which is SEM, including latent variables representing the complex effects of various measured variables. Factor-analysis application in establishing SEM can be divided into exploratory factor analysis and confirmatory factor analysis. The confirmatory factor analysis-based SEM starts with the initial model constructed by the relationships that are already known, or a designed hypothesis. Then, the model is verified with the fitness indices, and the causal path in the model is tested for its significance. On the other hand, exploratory factor analysis performs factor analysis to find out and set the latent variables. This approach has the disadvantage of not being able to fully reflect the theoretical causal relation because it constitutes a causal relation depending on the measured data. The structural equation model established in this study was based on exploratory factor analysis because the latent variables were deduced by factor analysis and they formed the basic \begin{table}
\begin{tabular}{c c c} \hline \hline
**Index** & **Acceptance Level** & **Classification** \\ \hline Q value & \textless{}3: Excellent & Parsimony-fit indices \\ Goodness of Fit Index (GFI) & \textgreater{}0.9: Excellent & Absolute-fit indices \\  & \textless{}0.05: Excellent & \\ Root Mean Square Error of Approximation (RMSEA) & \textless{}0.08: Good & Absolute-fit indices \\  & \textless{}0.1: Normal & \\ Adjusted Goodness of Fit Index (AGFI) & \textgreater{}0.8: Good & Absolute-fit indices \\ CFI & \textgreater{}0.9: Excellent & Incremental-fit indices \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Evaluation indices about structure goodness. Figure 1: Path types used in a path model. (**a**) causal; (**b**) both-way causal; (**c**) correlate; (**d**) independent path. structure of the SEM. When the theoretical causal relationships are not clear, this procedure is regarded as a rational approach in both natural-science [27; 41] and social fields [42]. The structural equation model consists of factors and their causal relationships forming the structure model as described in Figure 2, and the measured variables (x1, x2,... ) related to latent variables with their observed error terms form the observed model. Here, the factor is also called a latent variable, which implies the combined effect of the observed variable. Each measurement variable contains an error, which means the extent to which the latent variable cannot be fully explained by measurement variables. This error comes from the measurement process and it is one of the main reasons for using latent variables in SEM. The advantage of the structural equation is that it can analyze the combined effects of a large number of factors. In addition, a measurement error can be considered and its size can be derived. However, since the assumption that data must follow a normal distribution is satisfied, the more data, the more preferable it is [35]. The fitness index to examine the suitability of the constructed SEM can be divided into three categories, absolute-fit indices, parsimony-fit indices, and incremental-fit indices (Table 2).