## 3 Results
### Model Fit Assessment  
The initial model tested was concluded to identify and interpret the results of the fit index from the model estimation. There are many ways to interpret the fit model tested by looking at CMIN/df, RMSEA, GFI, and AGFI [78]. The model is also tested by evaluating TLI, NFI, and CFI, as well as parsimonious fit with PNFI and PGFI reference indicators [61]. This study used the three methods to test model fit (Table 6).  
The data processing results using AMOS software showed that the Chi-square value is 429,521 with 248 degrees of freedom. This indicates that the CMIN/df value is 1.75 in the limit between 1 \(<\) x \(<\) 3. Furthermore, many studies suggest checking the goodness-of-fit index by considering the variance and covariance predicted in the reproduced matrix [65; 79]. A higher GFI value of 0.80 is acceptable because it indicates a better model fit. In this study, the GFI value reached 0.88. Unlike the Chi-square, the RMSEA reference indicator considers the estimated parameters but not the sample size. Therefore, an RMSEA value less than 0.08 indicates an accepted model [80], and an RMSEA value smaller than 0.05 indicates a perfect model. This study's RMSEA value is 0.06, implying an acceptable model. For incremental fit measurement, the values of NFI, CFI, and AGFI must be greater than 0.8 to achieve an acceptable fit model [81]. In this study, the values of NFI, CFI, and AGFI exceeded 0.8. Some of the literature uses parsimonious fit measurement to determine model fit. A parsimonious model is the simplest and sharpest model to explain the analyzed phenomenon [82]. In this study, the entire parsimonious fit index exceeded 0.05, signifying the model is suitable. These three fit model tests confirm that the proposed structural model in Figure 2 is acceptable and appropriate for analyzing and interpreting the factors related to student satisfaction toward STEM education.  
The linear correlation coefficient (R2) [83] is the most important value for determining whether the proposed structural model strongly explains the factors influencing student satisfaction toward STEM education. [PERSON] [84] stated that an R2 value greater than 0.26  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Measurement** & **Indicator** & _p-Value_ & **Recommended Criteria** \\ \hline Absolute fit & CMIN/df & 1.75 & 1 \(<\) x \(<\) 3 \\ \hline  & GFI & 0.88 & \(>\)0.8 \\ \hline  & RMSEA & 0.06 & \(<\)0.08 \\ \hline  & RMR & 0.05 & \(<\)0.08 \\ \hline  & NFI & 0.85 & \(>\)0.8 \\ \hline Incremental fit & CFI & 0.80 & \(>\)0.8 \\ \hline  & AGFI & 0.82 & \(>\)0.8 \\ \hline Parsimonious fit & PNFI & 0.29 & \(>\)0.05 \\ \hline  & PGFI & 0.60 & \(>\)0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Absolute, incremental, and parsimonious fit measurement.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **PU** & **CL** & **PLAY** & **FC** & **SN** & **PEU** & **ATT** & **ACC** & **SAT** \\ \hline ATT & 0.276 & 0.269 & 0.338 & 0.278 & 0.278 & 0.315 & **0.383** & & \\ \hline ACC & 0.298 & 0.311 & 0.358 & 0.288 & 0.276 & 0.296 & 0.372 & **0.442** & \\ \hline SAT & 0.259 & 0.250 & 0.318 & 0.262 & 0.265 & 0.302 & 0.352 & 0.355 & **0.373** \\ \hline \hline \end{tabular}
\end{table}
Table 5: _Cont._(26%) was strong enough to explain a model. In this study, the R-value for acceptance toward STEM education was 78.4%, student attitude toward STEM education was 73.0%, and student satisfaction toward STEM education was 70.4%. Therefore, the proposed model explains the factors related to student satisfaction with STEM education.