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A modeling approach to identify academically resilient students: evidence from PIRLS 2016
Department of Education and Special Education, University of Gothenburg, Box 300, 40530, Gothenburg (SWE).ORCID iD: 0000-0002-2051-7248
University West, Department of Social and Behavioural Studies, Division for Educational Science and Languages. Department of Education and Special Education, University of Gothenburg, Box 300, 40530, Gothenburg (SWE).ORCID iD: 0000-0001-7071-2482
University West, Department of Social and Behavioural Studies, Division for Educational Science and Languages.ORCID iD: 0000-0002-7751-3942
2023 (English)In: European Journal of Psychology of Education, ISSN 0256-2928, E-ISSN 1878-5174, p. -20Article in journal (Refereed) Epub ahead of print
Abstract [en]

In studies of academic resilience, the concept is typically operationalized by pre-defned cutof values of students’ achievement level and their social background. A threat to the validity of such arbitrary operationalizations is that students around the cut of values may be misclassifed. The main objective of the current study is to apply a modeling approach to identify academically resilient students. Data come from the Progress in International Reading Literacy Study 2016. The primary method used was latent profle analysis. The study demonstrates that it is possible to identify a group of academically resilient students using a set of variables indicating achievement level and protective factors. Even though results suggest that academically resilient students have higher reading achievement compared to other less-advantaged students, it is evident that they have higher interest and confidence in reading than the non-resilient groups. A sensitivity analysis for other countries indicated similar results. Implications stemming from the results are discussed.

Place, publisher, year, edition, pages
2023. p. -20
Keywords [en]
Academic resilience, Sweden, PIRLS, Latent profle analysis
National Category
Pedagogical Work
Identifiers
URN: urn:nbn:se:hv:diva-20293DOI: 10.1007/s10212-023-00711-7ISI: 001010077300001Scopus ID: 2-s2.0-85162045475OAI: oai:DiVA.org:hv-20293DiVA, id: diva2:1780519
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Available from: 2023-07-06 Created: 2023-07-06 Last updated: 2024-01-08

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Yang Hansen, KajsaThorsen, Cecilia

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