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

  • Rosa Arboretti
  • Riccardo Ceccato
  • Luigi Salmaso

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.

  • Keywords:
  • Nonparametric permutation,
  • Evaluation of Educational Systems,
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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

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  2. Basso, D., Salmaso, L. (2011). A permutation test for umbrella alternatives. Statistics and Computing, 21(1), pp. 45–54. DOI: 10.1007/s11222-009-9145-8
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  5. Finos, L., Salmaso, L., Solari, A. (2007). Conditional inference under simultaneous stochastic ordering constraints. Journal of statistical planning and inference, 137(8), pp. 2633–2641. DOI: 10.1016/j.jspi.2006.04.014
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Chapter Information

Chapter Title

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

Authors

Rosa Arboretti, Riccardo Ceccato, Luigi Salmaso

Language

English

DOI

10.36253/978-88-5518-304-8.05

Peer Reviewed

Publication Year

2021

Copyright Information

© 2021 Author(s)

Content License

CC BY 4.0

Metadata License

CC0 1.0

Bibliographic Information

Book Title

ASA 2021 Statistics and Information Systems for Policy Evaluation

Book Subtitle

Book of short papers of the opening conference

Editors

Bruno Bertaccini, Luigi Fabbris, Alessandra Petrucci

Peer Reviewed

Publication Year

2021

Copyright Information

© 2021 Author(s)

Content License

CC BY 4.0

Metadata License

CC0 1.0

Publisher Name

Firenze University Press

DOI

10.36253/978-88-5518-304-8

eISBN (pdf)

978-88-5518-304-8

eISBN (xml)

978-88-5518-305-5

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Proceedings e report

Series ISSN

2704-601X

Series E-ISSN

2704-5846

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