## Materials and Methods
### Structural Equation Model
#### 2.2.2 Structural Equation Model
The absolute fit index is an index of how well the research model reflects input data. It can be said that the developed model itself is evaluated without comparing with other models. The chi-square statistic is often used as a representative. However, chi-square statistic has a disadvantage of underestimating the fit as the size of the sample increases. As an alternative to chi-square, RMSEA (root mean square error of approximation) is a value adjusted by the chi-square statistic to the degree of freedom and sample size and has been used in many studies recently. The RMSEA of 0.05 or less means very good fitness and below 0.8 means good fitness. If it is less than 0.1, it can be said to be normal. GFI (goodness of fit index), an index used in this study as an absolute fit index, is the most widely used fit index in the structural equation model. GFI is also indirectly affected by the size of the samples as well as the chi-square statistic. In order to compensate for this, AGFI (adjusted goodness of fit index) is used as an indicator that the GFI value is modified by using the degree of freedom of the model. Both GFI and AGFI have values between 0 and 1, and 0.9 and 0.8, respectively, indicate a good fit of the model. The Incremental fit index is an index indicating how well the research model reflects the input data than the null model, unlike the absolute fit index that evaluates the model itself. The most representative index is the NFI (normed fit index), but NFI has the disadvantage of less sensitivity to the complexity of the model. Therefore, CFI (comparative fit index) can be used as modified NFI. Parsimony fit index means the degree to which the model reaches the maximum degree of fit required fitness for each estimated coefficient. As a representative parsimony fit index, there is Q-value used in this study, which is a value obtained by dividing the chi-square value by degree of freedom to compensate for the disadvantage of the chi-square statistic. The absolute fit index, the incremental fit index, and the parsimony fit index evaluate the model by different criteria. When evaluating one structural equation model, it is not desirable to evaluate by  
Figure 2: Basic concept of structural equation model. only one kind of index. In most studies, two or more indices from a least two categories are applied and evaluated. In this study, five indexes were evaluated in three categories to evaluate the fitness of the structural equation model to be constructed more carefully using data with high uncertainty. This research adapted the Q value, goodness of fit (GFI), and root mean square error of approximation (RMSEA) among absolute-fit indices. The adjusted GFI (AGFI) of parsimony-fit indices and the comparative fit index (CFI) as an incremental-fit index were also adapted for checking compliance with various criteria. GFI and AGFI were suggested by [PERSON] and [PERSON] [37], and they can be regarded as the most popular indices to test SEM fitness. If the GFI value is greater than 0.9, it can be generally judged as properly constructed [33; 42; 43; 44]. An AGFI value larger than 0.9 is seen as \"excellent fitness\", but over 0.8 can be \"acceptable\" [45; 46]. The smaller the value of RMSEA, the most popular index, the better [47], but no larger than 0.06 [42; 44; 48] or 0.08 [20; 43; 49; 50]. The CFI, the most frequently used incremental index, guarantees the model's fitness when greater than 0.8 [49] or 0.9 [36; 50], and best close to the value of 1.0 [20; 50; 51; 52]. In this research, AMOS v.20.0 (IBM Statistics, Inc.) was used for path modeling and the SEM.