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Nonparametric methods for stratified C-sample designs: a case study

Rosa Arboretti
University of Padua, Italy - ORCID: 0000-0003-1263-0440

Riccardo Ceccato
University of Padua, Italy - ORCID: 0000-0002-8629-8439

Luigi Salmaso
University of Padua, Italy - ORCID: 0000-0001-6501-1585


ABOUT THIS CHAPTER

Several parametric and nonparametric methods have been proposed to deal with stratified C-sample problems where the main interest lies in evaluating the presence of a certain treatment effect, but the strata effects cannot be overlooked. Stratified scenarios can be found in several different fields. In this paper we focus on a particular case study from the field of education, addressing a typical stochastic ordering problem in the presence of stratification. We are interested in assessing how the performance of students from different degree programs at the University of Padova change, in terms of university credits and grades, when compared with their entry test results. To address this problem, we propose an extension of the Non-Parametric Combination (NPC) methodology, a permutation-based technique (see Pesarin and Salmaso, 2010), as a valuable tool to improve the data analytics for monitoring University students’ careers at the School of Engineering of the University of Padova. This new procedure indeed allows us to assess the efficacy of the University of Padova’s entry tests in evaluating and selecting future students.
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Keywords: Nonparametric permutation, Evaluation of Educational Systems

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Pages: 17-22

Published by: Firenze University Press

Publication year: 2021

DOI: 10.36253/978-88-5518-304-8.05

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Publication year: 2021

DOI: 10.36253/978-88-5518-304-8.05

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References

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Arboretti, R.; Ceccato, R.; Salmaso, L.; 2021; Nonparametric methods for stratified C-sample designs: a case study. Firenze, Firenze University Press.


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