#### 3.1.2 Exploratory Factor Analysis of Teaching Practice  
The KMO is 0.958, which indicates excellent sampling adequacy to perform a factor analysis since the items are highly correlated with each other. In addition, [PERSON]'s test of sphericity is significant (\(p\) = 0.000), confirming that there are sufficient correlations among the variables to justify factor analysis. Taken together, these results suggest that further factor analysis is appropriate and is expected to provide a good representation of the underlying structure of the data. In Table 2, it is shown that the highest commonality corresponds to item PD1, with a value of 0.755, indicating that 75.5% of its variance is explained by the extracted factors, meaning that item PD1 is well-represented by the common factors and contributes significantly to the model. On the other hand, the lowest communality is that of item PD7, with a value of 0.372, indicating that only 37.2% of its variance is explained by the factors, meaning that item PD7 has a low representation in the factorial model, which makes it a candidate for revision or possible elimination since its contribution to the model is limited. In Table 3, the results of the analysis of total variance explained show that the first six components have eigenvalues greater than 1, indicating that these factors are significant and explain 50.86% of the total variance before rotation. The first component alone explains 31.82%, but after rotation, its contribution decreases to 9.74%, while subsequent components, such as the second and third, increase their explained variance to 7.14% and 5.78%, respectively. This reflects a better distribution of variance among the components after rotation, facilitating their interpretation. The remaining components, starting with the seventh, explain minimal proportions of the variance, suggesting that they are not relevant to the model. Together, these six factors account for a significant amount of the variance and allow for a more balanced interpretation after rotation. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Item** & **Community** & **Item** & **Community** & **Item** & **Community** & **Item** & **Community** \\ \hline PD1 & 0.755 & PD11 & 0.456 & PD21 & 0.570 & PD31 & 0.517 \\ \hline PD2 & 0.747 & PD12 & 0.478 & PD22 & 0.528 & PD32 & 0.414 \\ \hline PD3 & 0.557 & PD13 & 0.418 & PD23 & 0.584 & PD33 & 0.522 \\ \hline PD4 & 0.544 & PD14 & 0.416 & PD24 & 0.594 & PD34 & 0.541 \\ \hline PD5 & 0.741 & PD15 & 0.401 & PD25 & 0.445 & PD35 & 0.425 \\ \hline PD6 & 0.718 & PD16 & 0.404 & PD26 & 0.503 & \\ \hline PD7 & 0.372 & PD17 & 0.454 & PD27 & 0.503 & \\ \hline PD8 & 0.397 & PD18 & 0.387 & PD28 & 0.497 & \\ \hline PD9 & 0.501 & PD19 & 0.449 & PD29 & 0.468 & \\ \hline PD10 & 0.479 & PD20 & 0.486 & PD30 & 0.529 & \\ \hline \end{tabular}
\end{table} 
Table 2: Initial communalities of the teaching practice items. Table 4 shows the factor loadings of the items on the underlying factors obtained using a principal components analysis with Promax rotation (an oblique rotation that allows for correlation between factors). Each item has a significant loading on one of the five extracted factors, suggesting the presence of five different latent constructs. All factor loadings are greater than 0.50, which gives it a better interpretation. The following factors are included:  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Component**} & \multicolumn{3}{c}{**Initial Eigenvalues**} & \multicolumn{3}{c}{**Sums of Squared**} & \multicolumn{3}{c}{**Sums of Loads**} \\  & \multicolumn{3}{c}{**Extraction Charges**} & \multicolumn{3}{c}{**Squared by Rotation**} \\ \cline{2-7}  & **Total** & **\% Variance** & **\% Accumulated** & **Total** & **\% Variance** & **\% Accumulated** & **Total** \\ \hline
1 & 11.1  
Factor 1 \"Teacher interaction\" is composed of the items PD1 (0.909), PD2 (0.919), and PD3 (0.599).