This study proposes a multi-level approach to identify both superficial and latent relationships among variables in the data setobtained from a vocational rehabilitation (VR) services program of people with significant disabilities. In our study,classification models are first used to extract the superficial relationships between dependent and independent variables at thefirst level, and association rule mining algorithms are employed to extract additional sets of interesting associativerelationships among variables at the second level. Finally, nonlinear nonparametric canonical correlation analysis (NLCCA)along with clustering algorithm is employed to identify latent nonlinear relationships. Experimental outputs validate theusefulness of the proposed approach
