Transformation and classification of ordinal survey data

Currently, machine learning is being significantly used in almost all of the research domains; however, its applicability in survey research is still in its infancy. In this paper, we attempt to highlight the applicability of machine learning in survey research while working on two different aspects in parallel. First, we introduce a pattern-based transformation method for ordinal survey data. Our purpose for developing such a transformation method is two-fold: our transformation facilitates the easy interpretation of ordinal survey data and provides convenience while applying standard machine-learning approaches; and second, we demonstrate the application of various classification techniques over real and transformed ordinal survey data and interpret their results in terms of their suitability in survey research. Our experimental results suggest that machine learning coupled with a pattern-recognition paradigm has tremendous scope in survey research.