MFACT can operate under any specification of matrix D.
This means that no probabilistic results shall arise from an MFACT exercise and, therefore, we will not make any kind of statistical inference from the dataset.
The results for each separate analysis reveal a rich variance structure for each group of variables, providing strong evidence for a MFACT approach.
We group cities that share the same characteristics along the five principal axes we found in the MFACT step.
MFACT results suggest that this cluster groups cities that are, on a statistical basis, different from the others because of their outstandingly low educational attainment and their poor economic and social perspectives that influence on participation and human capital accumulation decisions.
Following Elhorst (2003), we studied a large dataset in order to establish similarities and differences between Colombian cities based on principal axes methods (MFACT, Becue-Bertaut and Pages 2004, 2008), clustering techniques and statistical criteria (Husson et al., 2010).