## 4 Survey Exploratory Analysis
### Categorical Principal Component Analysis  
The CATPCA solution was computed iteratively from the data, using the optimal scaling process of IBM SPSS to quantify the variables. Both ordinal and spline ordinal levels were considered, with the highest percentage of total variance being explained with the ordinal-level analysis. This is consistent with the recommendation to perform an ordinal-level analysis when the number of categories is small, and a monotonic spline-level analysis when the number of categories is large compared to the number of respondents [53].  
CATPCA grouped the 18 variables into four principal components, which explained 71.179% of the total variance (see Tables 11 and 12). A high value of 0.976 for [PERSON]'s alpha was also obtained for both CATPCA models, with and without rotation. This technique was able to identify the principal components or dimensions more clearly from the set of ordinal variables in this survey (see Table 13). The first principal component was strongly associated with the variables G1, G2, C2 and D2, and represented the usefulness of MaGraDa in terms of learning graphs. The second principal component was strongly associated with the variables E1, E2, E3, E4 and E5, and explained its user-friendliness and usability. The third principal component was primarily associated with variables D1, D3, D4, D5 and D6, and was therefore related to the impact of MaGraDa on academic performance. Finally, the fourth principal component, which was highly associated with variables C1, C3, C4, E1, E2, D1 and G3, was related to the quality of its content (procedures, algorithms and documentation). In summary, the CATPCA results showed that our survey comprehensively covered the most important aspects that, in our opinion, an educational software should encompass, that is, its usefulness for learning, its usability and user-friendliness, its effect on academic performance, and its content quality.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Item**} & **Dimension 1:** & **Dimension 2:** & **Dimension 3:** & **Dimension 4:** \\  & **Usefulness for** & **Usability and** & **Academic Performance** & **Content Quality** \\  & **Learning** & **User-Friendliness** & **Performance** & **Performance** \\ \hline
**E1** & 0.053 & 0.640 & \(-\)0.213 & 0.501 \\
**E2** & \(-\)0.002 & 0.561 & \(-\)0.138 & 0.646 \\
**E3** & 0.076 & 0.763 & 0.111 & 0.168 \\
**E4** & \(-\)0.041 & 0.915 & 0.089 & \(-\)0.069 \\
**E5** & \(-\)0.038 & 0.923 & 0.048 & \(-\)0.065 \\
**C1** & \(-\)0.027 & 0.048 & 0.229 & 0.640 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Rotated component loadings from CATPCA, varimax rotation (see Table 3 for a description of each variable).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Dimension**} & \multicolumn{3}{c}{**Variance Accounted for**} \\  & **Cronbach’s Alpha** & **Total (Eigenvalue)** & **\% of Variance** \\ \hline
1 & 0.855 & 5.185 & 28.807 \\
2 & 0.764 & 3.590 & 19.945 \\
3 & 0.616 & 2.392 & 13.291 \\
4 & 0.415 & 1.644 & 9.136 \\
**Total** & 0.976 & 12.812 & 71.179 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Categorical PCA (CATPCA) model summary.