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Bayesian networks and the assessment of universities' value added

Author

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  • F. Cugnata
  • G. Perucca
  • S. Salini

Abstract

A broad literature focused on the effectiveness of tertiary education. In classical models, a performance indicator is regressed on a set of characteristics of the individuals and fixed effects at the institution level. The FE coefficients are interpreted as the pure value added of the universities. The innovative contribution of the present paper resides in the use of Bayesian network (BN) analysis to assess the effectiveness of tertiary education. The results of an empirical study focused on Italian universities are discussed, to present the use of BN as a decision support tool for policy-making purposes.

Suggested Citation

  • F. Cugnata & G. Perucca & S. Salini, 2017. "Bayesian networks and the assessment of universities' value added," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(10), pages 1785-1806, July.
  • Handle: RePEc:taf:japsta:v:44:y:2017:i:10:p:1785-1806
    DOI: 10.1080/02664763.2016.1223839
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    3. Massimo Florio & Francesco Giffoni & Anna Giunta & Emanuela Sirtori, 2018. "Big science, learning, and innovation: evidence from CERN procurement," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 27(5), pages 915-936.

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