IDEAS home Printed from https://ideas.repec.org/a/spr/stmapp/v31y2022i1d10.1007_s10260-021-00563-9.html
   My bibliography  Save this article

Studying the relationship between anxiety and school achievement: evidence from PISA data

Author

Listed:
  • Antonella D’Agostino

    (Università degli Studi di Napoli Parthenope)

  • Francesco Schirripa Spagnolo

    (Università degli Studi di Pisa)

  • Nicola Salvati

    (Università degli Studi di Pisa)

Abstract

Using the Programme for International Student Assessment (PISA) 2015 data for Italy, this paper offers a complete overview of the relationship between test anxiety and school performance by studying how anxiety affects the performance of students along the overall conditional distribution of mathematics, literature and science scores. We aim to indirectly measure whether higher goals increase test anxiety, starting from the hypothesis that high-skilled students generally set themselves high goals. We use an M-quantile regression approach that allows us to take into account the hierarchical structure and sampling weights of the PISA data. There is evidence of a negative and statistically significant relationship between test anxiety and school performance. The size of the estimated association is greater at the upper tail of the distribution of each score than at the lower tail. Therefore, our results suggest that high-performing students are more affected than low-performing students by emotional reactions to tests and school-work anxiety.

Suggested Citation

  • Antonella D’Agostino & Francesco Schirripa Spagnolo & Nicola Salvati, 2022. "Studying the relationship between anxiety and school achievement: evidence from PISA data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(1), pages 1-20, March.
  • Handle: RePEc:spr:stmapp:v:31:y:2022:i:1:d:10.1007_s10260-021-00563-9
    DOI: 10.1007/s10260-021-00563-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10260-021-00563-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10260-021-00563-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Contini, Dalit & Tommaso, Maria Laura Di & Mendolia, Silvia, 2017. "The gender gap in mathematics achievement: Evidence from Italian data," Economics of Education Review, Elsevier, vol. 58(C), pages 32-42.
    2. Sergio Longobardi & Margherita Maria Pagliuca & Andrea Regoli, 2018. "Can problem-solving attitudes explain the gender gap in financial literacy? Evidence from Italian students’ data," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(4), pages 1677-1705, July.
    3. Massimiliano Bratti & Daniele Checchi & Antonio Filippin, 2007. "Geographical Differences in Italian Students' Mathematical Competencies: Evidence from Pisa 2003," Giornale degli Economisti, GDE (Giornale degli Economisti e Annali di Economia), Bocconi University, vol. 66(3), pages 299-333, November.
    4. Bratti, Massimiliano & Checchi, Daniele & Filippin, Antonio, 2007. "Territorial Differences in Italian Students’ Mathematical Competencies: Evidence from PISA 2003," IZA Discussion Papers 2603, Institute of Labor Economics (IZA).
    5. Daniele Checchi, 2004. "Da dove vengono le competenze scolastiche?," Stato e mercato, Società editrice il Mulino, issue 3, pages 413-454.
    6. Nicole Schneeweis, 2011. "Educational institutions and the integration of migrants," Journal of Population Economics, Springer;European Society for Population Economics, vol. 24(4), pages 1281-1308, October.
    7. Nicole Schneeweis & Rudolf Winter-Ebmer, 2008. "Peer effects in Austrian schools," Studies in Empirical Economics, in: Christian Dustmann & Bernd Fitzenberger & Stephen Machin (ed.), The Economics of Education and Training, pages 133-155, Springer.
    8. Francesco Schirripa Spagnolo & Nicola Salvati & Antonella D’Agostino & Ides Nicaise, 2020. "The use of sampling weights in M‐quantile random‐effects regression: an application to Programme for International Student Assessment mathematics scores," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(4), pages 991-1012, August.
    9. Nikos Tzavidis & Nicola Salvati & Timo Schmid & Eirini Flouri & Emily Midouhas, 2016. "Longitudinal analysis of the strengths and difficulties questionnaire scores of the Millennium Cohort Study children in England using M-quantile random-effects regression," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(2), pages 427-452, February.
    10. Masci, Chiara & Johnes, Geraint & Agasisti, Tommaso, 2018. "Student and school performance across countries: A machine learning approach," European Journal of Operational Research, Elsevier, vol. 269(3), pages 1072-1085.
    11. Mariagiulia Matteucci & Stefania Mignani, 2014. "Exploring Regional Differences in the Reading Competencies of Italian Students," Evaluation Review, , vol. 38(3), pages 251-290, June.
    12. Tommaso Agasisti & Francesca Ieva & Anna Maria Paganoni, 2017. "Heterogeneity, school-effects and the North/South achievement gap in Italian secondary education: evidence from a three-level mixed model," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(1), pages 157-180, March.
    13. Giambona, Francesca & Porcu, Mariano, 2015. "Student background determinants of reading achievement in Italy. A quantile regression analysis," International Journal of Educational Development, Elsevier, vol. 44(C), pages 95-107.
    14. Susana Faria & Maria Conceição Portela, 2016. "Student Performance in Mathematics using PISA-2009 data for Portugal," Working Papers de Gestão (Management Working Papers) 01, Católica Porto Business School, Universidade Católica Portuguesa.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Francesco Schirripa Spagnolo & Nicola Salvati & Antonella D’Agostino & Ides Nicaise, 2020. "The use of sampling weights in M‐quantile random‐effects regression: an application to Programme for International Student Assessment mathematics scores," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(4), pages 991-1012, August.
    2. Orazio Giancola & Luca Salmieri, 2020. "Family Background, School-Track and Macro-Area: the Complex Chains of Education Inequalities in Italy," Working Papers 4/20, Sapienza University of Rome, DISS.
    3. Silvia Bianconcini & Stefania Mignani & Jacopo Mingozzi, 2023. "Assessing maths learning gaps using Italian longitudinal data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(3), pages 911-930, September.
    4. Ralph Hippe & Maciej Jakubowski & Luisa De Sousa Lobo Borges de Araujo, 2018. "Regional inequalities in PISA: the case of Italy and Spain," JRC Research Reports JRC109057, Joint Research Centre.
    5. Giambona, Francesca & Porcu, Mariano, 2018. "School size and students' achievement. Empirical evidences from PISA survey data," Socio-Economic Planning Sciences, Elsevier, vol. 64(C), pages 66-77.
    6. Tindara Addabbo & Maddalena Davoli & Marina Murat, 2018. "Is there an immigrant-gender gap in education? An empirical investigation based on PISA data from Italy," Department of Economics 0124, University of Modena and Reggio E., Faculty of Economics "Marco Biagi".
    7. Masci, Chiara & Ieva, Francesca & Agasisti, Tommaso & Paganoni, Anna Maria, 2016. "Does class matter more than school? Evidence from a multilevel statistical analysis on Italian junior secondary school students," Socio-Economic Planning Sciences, Elsevier, vol. 54(C), pages 47-57.
    8. Camanho, Ana S. & Varriale, Luisa & Barbosa, Flávia & Sobral, Thiago, 2021. "Performance assessment of upper secondary schools in Italian regions using a circular pseudo-Malmquist index," European Journal of Operational Research, Elsevier, vol. 289(3), pages 1188-1208.
    9. Simona Ferraro & Tommaso Agasisti & Francesco Porcelli & Mara Soncin, 2021. "Local governments’ efficiency and educational results: empirical evidence from Italian primary schools," Applied Economics, Taylor & Francis Journals, vol. 53(35), pages 4017-4039, July.
    10. Borgna, Camilla & Struffolino, Emanuela, 2017. "Pushed or pulled? Girls and boys facing early school leaving risk in Italy," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 61, pages 298-313.
    11. Sulis, Isabella & Giambona, Francesca & Porcu, Mariano, 2020. "Adjusted indicators of quality and equity for monitoring the education systems over time. Insights on EU15 countries from PISA surveys," Socio-Economic Planning Sciences, Elsevier, vol. 69(C).
    12. Cavalieri, Marina & Finocchiaro Castro, Massimo & Guccio, Calogero, 2023. "Organised crime and educational outcomes in Southern Italy: An empirical investigation," Socio-Economic Planning Sciences, Elsevier, vol. 89(C).
    13. Marina Cavalieri & Massimo Finocchiaro Castro & Calogero Guccio, 2020. "Does the Fish Rot from the Head? Organised Crime and Educational Outcomes in Southern Italy," Working papers 97, Società Italiana di Economia Pubblica.
    14. Ferraro, Simona & Põder, Kaire, 2018. "School-level policies and the efficiency and equity trade-off in education," Journal of Policy Modeling, Elsevier, vol. 40(5), pages 1022-1037.
    15. Adriana Di Liberto & Fabiano Schivardi & Giovanni Sulis, 2015. "Managerial practices and student performance," Economic Policy, CEPR, CESifo, Sciences Po;CES;MSH, vol. 30(84), pages 683-728.
    16. Elke Lüdemann, 2011. "Schooling and the Formation of Cognitive and Non-cognitive Outcomes," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 39.
    17. Barra, Cristian & Boccia, Marinella, 2019. "“The determinants of students' achievement: a difference between OECD and not OECD countries”," MPRA Paper 92561, University Library of Munich, Germany.
    18. Sevda Gürsakal & Dilek Murat & Necmi Gürsakal, 2016. "Assessment of PISA 2012 Results With Quantile Regression Analysis Within The Context of Inequality In Educational Opportunity," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 4(2), pages 41-54, September.
    19. Tommaso Agasisti & Veronica Minaya, 2018. "Evaluating the Stability of School Performance Estimates for School Choice: Evidence for Italian Primary Schools," Working papers 67, Società Italiana di Economia Pubblica.
    20. Michela Ponzo, 2011. "The effects of school competition on the achievement of Italian students," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 32(1), pages 53-61, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:stmapp:v:31:y:2022:i:1:d:10.1007_s10260-021-00563-9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.