## 4 Results
### PLS Analysis  
In this study, SmartPLS is used to process the original data from SPSS to CSV file. SmartPLS is advantageous for samples smaller than 100. There are a total of 94 participants in the survey, which is an appropriate sample size. Reasons for analyzing the data with this tool include small sample size, non-normal data, formative measures, focus on prediction, model complexity, exploratory research, and theory development. There are reflective, formative, and redundancy models in the PLS. From previous studies, a model that shows the effect level of the latent variables of teaching, counseling, administration, equipment, and retention on each other and their ratios to each other was put forward. Figure 1: Basic statistics analysis: mean scores of the factors; the error bars denote standard deviation. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Factor** & **Cronbach Alpha** & **AVE** & **Mean** & **SD** & **Teaching** & **Counseling** & **Administration** & **Equipment** \\ \hline Teaching & 0.886 & 0.581 & 4.276 & 0.631 & 0.762 & 0.671 & 0.525 & 0.433 \\ Counseling & 0.942 & 0.600 & 4.674 & 0.581 & 0.671 & 0.775 & 0.671 & 0.467 \\ Administration & 0.912 & 0.606 & 4.761 & 0.510 & 0.525 & 0.671 & 0.778 & 0.464 \\ Equipment & 0.881 & 0.658 & 4.298 & 0.829 & 0.433 & 0.467 & 0.464 & 0.811 \\ \hline \hline \end{tabular} Note: AVE = average variance extracted; SD = standard deviation.  
\end{table} 
Table 3: Reliability and validity analyses. In Figure 2, all the outer loadings are higher than 0.7. In collinearity statistics, the inner variance inflation factor was smaller than 10. The four factors were not collinear in this research. The path coefficients of the prediction model were positive in teaching, counseling, and administration; however, they were negative in equipment to the latent variable of retention. The model also presented innovation (0.915) and passion (0.921), which had positive path coefficients to retention. In path coefficient analysis, the factor of teaching coefficient was higher than 0.7, the total effect \(p\)-value was smaller than 0.05, and t \(>\) 3.29 (Table 5). This indicates that the formative construct in teaching can explain at least 50% of the variance of retention. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Source of Variation**} & **SS** & **df** & **MS** & **F** & \(p\)**-Value** & **Post Hoc** \\ \hline \multirow{2}{*}{Teaching \(\ast\) Grade} & Between Group & 5.098 & 6 & 0.85 & 3.288 & 0.006 & \multirow{2}{*}{\(2>1\)} \\  & Within Group & 22.484 & 87 & 0.258 & & & \\ \hline \multirow{2}{*}{Teaching \(\ast\) Experience} & Between Group & 1.187 & 1 & 1.187 & 4.138 & 0.045 & \multirow{2}{*}{\(2>1\)} \\  & Within Group & 26.395 & 92 & 0.287 & & & \\ \hline \multirow{2}{*}{Teaching \(\ast\) EduB} & Between Group & 2.117 & 1 & 2.117 & 7.647 & 0.007 & \multirow{2}{*}{\(2\)} \\  & Within Group & 25.466 & 92 & 0.277 & & & \\ \hline \multirow{2}{*}{Counseling \(\ast\) EduB} & Between Group & 0.924 & 1 & 0.924 & 3.469 & 0.066 & \multirow{2}{*}{\(2\)} \\  & Within Group & 24.514 & 92 & 0.266 & & & \\ \hline \multicolumn{2}{c}{Note: \(p\)-value is significantly smaller than 0.05, SS = sum of squares; df = degree of freedom; MS = mean square; F = F value.} & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: ANOVA and post hoc analyses. Figure 2: Partial least squares structure model (inner path coefficients and outer weights). First, from Table 6, all the composite reliability values are around 0.8-0.9, supporting the internal consistency and reliability of the measures. Second, the values of convergence validity (AVE) were all higher than 0.5, and the values of composite reliability (CR and Cronbach's alpha CA) were all higher than 0.7. In discriminate validity, the coefficient was smaller than 0.7. Although the teaching coefficient was 0.873, it is smaller than 0.918. Therefore, it is still in the reasonable range. Considering redundancy, if it is higher, the model is a good fit (goodness of fit = Sqrt (redundancy)).