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dc.contributor.authorMazurek, Jiri
dc.contributor.authorFernández García, Carlos
dc.contributor.authorPérez Rico, Cristina
dc.date.accessioned2021-09-23T08:08:00Z
dc.date.available2021-09-23T08:08:00Z
dc.date.issued2021-09-21
dc.identifier.issn1508-2008
dc.identifier.urihttp://hdl.handle.net/11089/39153
dc.description.abstractThe aim of this paper was to investigate the relationship between countries’ PISA study results from 2018 and a set of indices related to socio-economic inequality, such as the Gini index, human development index, or gender inequality index, along with purely economic variables, such as GDP per capita and government expenditure on education. The study covered 70 countries, consisting of 37 OECD countries and 33 non-OECD countries. Research methods included multivariate linear regression models, k-means clustering, and hierarchical clustering. Our findings revealed that the Gini index was statistically insignificant, indicating income inequality had little effect on students’ PISA performance. On the other hand, the gender inequality index was the single most statistically significant explanatory variable for both OECD and non-OECD countries. Therefore, our recommendation for policymakers is simple: increase students’ PISA performance, thus enhancing countries’ human capital and competitiveness, and focus on decreasing gender disparity and the associated loss of achievement due to gender inequality.en
dc.description.abstractCelem tego artykułu było zbadanie związku między wynikami badania PISA przeprowadzonego w poszczególnych krajach w 2018 r. a zestawem wskaźników związanych z nierównościami społeczno-ekonomicznymi, takimi jak indeks Giniego, wskaźnik rozwoju społecznego czy wskaźnik nierówności płci, oraz ze zmiennymi czysto ekonomicznymi, takimi jak PKB per capita i wydatki rządowe na edukację. Badaniem objęto 70 krajów, w tym 37 krajów OECD i 33 kraje spoza OECD. Metody badawcze obejmowały wielowymiarowe modele regresji liniowej, grupowanie k‑średnich i grupowanie hierarchiczne. Wyniki przeprowadzonej analizy wykazały, że wskaźnik Giniego był statystycznie nieistotny, co wskazuje, że nierówności dochodowe miały niewielki wpływ na wyniki uczniów w badaniu PISA. Z drugiej strony, wskaźnik nierówności płci był jedyną najbardziej istotną statystycznie zmienną objaśniającą zarówno dla krajów OECD, jak i spoza OECD. Dlatego nasza rekomendacja dla decydentów jest prosta: należy zwiększyć wyniki uczniów w badaniu PISA, a tym samym osiągnąć poprawę w obszarze kapitału ludzkiego i konkurencyjności krajów, oraz skupić się na zmniejszaniu nierówności płci i związanej z tym utraty osiągnięć edukacyjnych wynikających z nierówności płci.pl
dc.language.isoen
dc.publisherWydawnictwo Uniwersytetu Łódzkiegopl
dc.relation.ispartofseriesComparative Economic Research. Central and Eastern Europe;3pl
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0
dc.subjecteducationen
dc.subjectgender inequality indexen
dc.subjectGini indexen
dc.subjectinequalityen
dc.subjectPISA 2018en
dc.subjectedukacjapl
dc.subjectwskaźnik nierówności płcipl
dc.subjectindeks Giniegopl
dc.subjectnierównościpl
dc.subjectPISApl
dc.titleInequality and Students’ PISA 2018 Performance: a Cross-Country Studyen
dc.title.alternativeNierówności a wyniki badania umiejętności uczniów PISA 2018: porównanie międzykrajowepl
dc.typeArticle
dc.page.number163-183
dc.contributor.authorAffiliationMazurek, Jiri - Ph.D., Silesian University in Opava, Opava, Czech Republicen
dc.contributor.authorAffiliationFernández García, Carlos - Ph.D., Universidad Tecnica de Ambato, Quito, Ecuadoren
dc.contributor.authorAffiliationPérez Rico, Cristina - Ph.D., Escuela Politecnica Nacional, Quito, Ecuadoren
dc.identifier.eissn2082-6737
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dc.contributor.authorEmailMazurek, Jiri - mazurek@opf.slu.cz
dc.contributor.authorEmailFernández García, Carlos - garciafernandez.c@gmail.com
dc.contributor.authorEmailPérez Rico, Cristina - cristina.perez@epn.edu.ec
dc.identifier.doi10.18778/1508-2008.24.27
dc.relation.volume24


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