Chapter Nonparametric methods for stratified C-sample designs: a case study
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....
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| Ngôn ngữ: | Tiếng Anh |
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Firenze University Press
2022
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| Truy cập trực tuyến: | ONIX_20220601_9788855183048_522 |
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| _version_ | 1869526157104250880 |
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| author | ARBORETTI GIANCRISTOFARO, ROSA Ceccato, Riccardo SALMASO, LUIGI |
| author_browse | ARBORETTI GIANCRISTOFARO, ROSA Ceccato, Riccardo SALMASO, LUIGI |
| author_facet | ARBORETTI GIANCRISTOFARO, ROSA Ceccato, Riccardo SALMASO, LUIGI |
| author_sort | ARBORETTI GIANCRISTOFARO, ROSA |
| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-82660 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Firenze University Press |
| publisherStr | Firenze University Press |
| record_format | ojs |
| spelling | doab-20.500.12854ir-826602022-06-02T04:14:54Z Chapter Nonparametric methods for stratified C-sample designs: a case study ARBORETTI GIANCRISTOFARO, ROSA Ceccato, Riccardo SALMASO, LUIGI Nonparametric permutation Evaluation of Educational Systems 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. 2022-06-02T04:14:54Z 2022-06-02T04:14:54Z 2022-06-01T12:19:49Z 2021 chapter ONIX_20220601_9788855183048_522 2704-5846 https://library.oapen.org/handle/20.500.12657/56337 9788855183048 https://directory.doabooks.org/handle/20.500.12854/82660 eng Proceedings e report open access image/jpeg Attribution 4.0 International https://library.oapen.org/bitstream/20.500.12657/56337/1/16976.pdf Firenze University Press 10.36253/978-88-5518-304-8.05 10.36253/978-88-5518-304-8.05 2ec4474d-93b1-4cfa-b313-9c6019b51b1a 9788855183048 6 Florence open access |
| spellingShingle | Nonparametric permutation Evaluation of Educational Systems ARBORETTI GIANCRISTOFARO, ROSA Ceccato, Riccardo SALMASO, LUIGI Chapter Nonparametric methods for stratified C-sample designs: a case study |
| title | Chapter Nonparametric methods for stratified C-sample designs: a case study |
| title_full | Chapter Nonparametric methods for stratified C-sample designs: a case study |
| title_fullStr | Chapter Nonparametric methods for stratified C-sample designs: a case study |
| title_full_unstemmed | Chapter Nonparametric methods for stratified C-sample designs: a case study |
| title_short | Chapter Nonparametric methods for stratified C-sample designs: a case study |
| title_sort | chapter nonparametric methods for stratified c sample designs a case study |
| topic | Nonparametric permutation Evaluation of Educational Systems |
| topic_facet | Nonparametric permutation Evaluation of Educational Systems |
| url | ONIX_20220601_9788855183048_522 |
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