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One Step Back or One Step Forward? Effects of Grade Retention and School Retention Composition on Portuguese Students’ Psychosocial Outcomes Using PISA 2018 Data

Author

Listed:
  • Joana Pipa

    (CIE-ISPA (Centre for Educational Research-ISPA), ISPA-Instituto Universtiário, Rua Jardim do Tabaco 34, 1149-041 Lisbon, Portugal)

  • Francisco Peixoto

    (CIE-ISPA (Centre for Educational Research-ISPA), ISPA-Instituto Universtiário, Rua Jardim do Tabaco 34, 1149-041 Lisbon, Portugal)

Abstract

Grade retention is a common practice applied to academically struggling students within the Portuguese context. Studies investigating the psychological experiences of grade-retained students are still scarce. In addition, most studies tend to neglect the multilevel nature of the school context. This study examines the effects of grade retention in grades 1–9 on Portuguese students’ psychosocial outcomes by the age of 15, using PISA 2018 data. Using a quasi-experimental design through full matching, we reduced the bias between 1362 retained and 4189 promoted students in relevant background variables. Results from the multilevel models showed that retained students, by the age of 15, present lower task orientation and school belonging. In addition, we found that the high retention rates negatively relate to students’ reading self-concept, task orientation, and school valuing and that school retention rates moderate the relationship between students’ retention and the psychosocial variables considered. Overall, these findings suggest detrimental effects of grade retention and that grade retention also affects the promoted peers of retained students.

Suggested Citation

  • Joana Pipa & Francisco Peixoto, 2022. "One Step Back or One Step Forward? Effects of Grade Retention and School Retention Composition on Portuguese Students’ Psychosocial Outcomes Using PISA 2018 Data," Sustainability, MDPI, vol. 14(24), pages 1-19, December.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:24:p:16573-:d:999580
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    1. van Buuren, Stef & Groothuis-Oudshoorn, Karin, 2011. "mice: Multivariate Imputation by Chained Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i03).
    2. Hong, Guanglei & Raudenbush, Stephen W., 2006. "Evaluating Kindergarten Retention Policy: A Case Study of Causal Inference for Multilevel Observational Data," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 901-910, September.
    3. Luis Pires & Rosa Santero-Sánchez & Cristian Macías, 2021. "School Failure in the Region of Madrid (Spain): An Approximation through Diagnostic Assessment in 2019," Sustainability, MDPI, vol. 13(17), pages 1-20, September.
    4. Miyako Ikeda & Emma García, 2014. "Grade repetition: A comparative study of academic and non-academic consequences," OECD Journal: Economic Studies, OECD Publishing, vol. 2013(1), pages 269-315.
    5. José M. Cordero & Víctor Cristóbal & Daniel Santín, 2018. "Causal Inference On Education Policies: A Survey Of Empirical Studies Using Pisa, Timss And Pirls," Journal of Economic Surveys, Wiley Blackwell, vol. 32(3), pages 878-915, July.
    6. Victor Lavy & M. Daniele Paserman & Analia Schlosser, 2012. "Inside the Black Box of Ability Peer Effects: Evidence from Variation in the Proportion of Low Achievers in the Classroom," Economic Journal, Royal Economic Society, vol. 122(559), pages 208-237, March.
    7. Agasisti, Tommaso & Cordero, Jose M., 2017. "The determinants of repetition rates in Europe: Early skills or subsequent parents’ help?," Journal of Policy Modeling, Elsevier, vol. 39(1), pages 129-146.
    8. Ho, Daniel & Imai, Kosuke & King, Gary & Stuart, Elizabeth A., 2011. "MatchIt: Nonparametric Preprocessing for Parametric Causal Inference," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i08).
    9. Hugo Reis & Manuel Coutinho Pereira, 2014. "Grade retention during basic education in Portugal: determinants and impact on student achievement," Economic Bulletin and Financial Stability Report Articles and Banco de Portugal Economic Studies, Banco de Portugal, Economics and Research Department.
    10. Luis Catela Nunes & Ana Balcão Reis & Carmo Seabra, 2018. "Is retention beneficial to low-achieving students? Evidence from Portugal," Applied Economics, Taylor & Francis Journals, vol. 50(40), pages 4306-4317, August.
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