Identifying Key Success Factors of Vocational Rehabilitation Services Program for People with Disabilities: A Multi-Level Analysis Approach

摘要

This study proposes a multi-level approach to identify both superficial and latent relationships among variables in the data set obtained 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 the first level, and association rule mining algorithms are employed to extract additional sets of interesting associative relationships 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 the usefulness of the proposed approach.