Use of Factorial Analysis of Mixed Data (FAMD) and Hierarchical Cluster Analysis on Principal Component (HCPC) for Multivariate Analysis of Academic Performance of Industrial Engineering Programs
The article describes a new idea about using Factor Analysis, Mixed Data, and Hierarchical Cluster Analysis on Principal Components to study the academic performance in 82 Industrial Engineering Programs in Colombia. For this, we used the data from the results of the standardized test of the Saber Pro.). The authors find that the first three components explain 89.12% of the original data set variability. The quantitative variables associated with the Factor Analysis are the first dimension, while the two qualitative variables are related to the second dimension. The first factor explains 95.83% of the dispersion of the scores in Critical Reading, 94.72% of the variability in Quantitative Reasoning, 94.51% of the variation in Mathematics and Statistics, among others. This study shows a strong positive correlation between the quantitative variables and the first factorial axis. It assumes that the Industrial Engineering Programs of public higher education institutions perform better than private ones. The article stipulates that the higher education institutions belonging to the Andean Region present a better performance, followed by the higher education institutions located in the Pacific Region. In general terms, the results confirm that the best performing universities usually appear in the first places in the different rankings and are located in the big cities.