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A Latent Class Conjoint Analysis for analysing graduates’ profiles
Last modified: 2018-05-22
Abstract
This paper aims to stabilize the relationship between universities and companies. Lombardy companies with at least 15 employees were asked them to manifest their preferences choosing among profiles of new graduates. A Latent Class Metric Conjoint Analysis is employed to evaluate the ideal graduate’s profile for a job position and to detect the existence of subgroups of companies having homogeneous preferences about such features.
References
1.DeSarbo,W.S,Wedel,M.A.,Vriens,M.,Ramaswamy,V.:Latent Class Metric Conjoint Analysis. Marketing Letters, 3(2), 137-288 (1992).
2. Dempster, A., Laird, N., Rubin, D.: Maximum Likelihood from Incomplete Data via the EM-Algorithm. Journal of the Royal Statistical Society: Series B, 39(1), 1-38 (1977).
3. Fabbris, L. and Scioni, M. Dimensionality of scores obtained with a paired-comparison tournament system of questionnaire item. In A. Meerman and T. Kliewe, eds., Academic Proceedings of the 2015 University-Industry Interaction Conference (2015).
4. McLachlan, G. J., Peel, D.: Finite Mixture Models. New York: Wiley (2000).
5. Wedel, M.A.: Concomitant variables in finite mixture models. Statistica Neerlandica, 56(3), 362-375 (2002).
2. Dempster, A., Laird, N., Rubin, D.: Maximum Likelihood from Incomplete Data via the EM-Algorithm. Journal of the Royal Statistical Society: Series B, 39(1), 1-38 (1977).
3. Fabbris, L. and Scioni, M. Dimensionality of scores obtained with a paired-comparison tournament system of questionnaire item. In A. Meerman and T. Kliewe, eds., Academic Proceedings of the 2015 University-Industry Interaction Conference (2015).
4. McLachlan, G. J., Peel, D.: Finite Mixture Models. New York: Wiley (2000).
5. Wedel, M.A.: Concomitant variables in finite mixture models. Statistica Neerlandica, 56(3), 362-375 (2002).
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