IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2021i19p2416-d645371.html
   My bibliography  Save this article

Inferential Reasoning of Secondary School Mathematics Teachers on the Chi-Square Statistic

Author

Listed:
  • Jesús Guadalupe Lugo-Armenta

    (Departamento de Ciencias Exactas, Universidad de Los Lagos, Osorno 5290000, Chile)

  • Luis Roberto Pino-Fan

    (Departamento de Ciencias Exactas, Universidad de Los Lagos, Osorno 5290000, Chile)

Abstract

Statistics education has investigated how to promote formal inferential reasoning from informal inferential reasoning. Nevertheless, there is still a need for proposals that explore and progressively develop inferential reasoning of students and teachers. Concerning this, the objective of this article is to characterize the inferential reasoning that secondary school mathematics teachers show in the practices that they develop to solve problems regarding the Chi-square statistic. To achieve this, we use theoretical and methodological notions introduced by the onto-semiotic approach of mathematics knowledge and instruction. In particular, we have taken a theoretical proposal of levels of inferential reasoning for the Chi-square statistic. Based on the results, the main conclusion was that the proposal above effectively predicted the teachers’ practices, allowing us to distinguish characteristic elements of the levels of inferential reasoning.

Suggested Citation

  • Jesús Guadalupe Lugo-Armenta & Luis Roberto Pino-Fan, 2021. "Inferential Reasoning of Secondary School Mathematics Teachers on the Chi-Square Statistic," Mathematics, MDPI, vol. 9(19), pages 1-20, September.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:19:p:2416-:d:645371
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/19/2416/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/9/19/2416/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. C. J. Wild & M. Pfannkuch, 1999. "Statistical Thinking in Empirical Enquiry," International Statistical Review, International Statistical Institute, vol. 67(3), pages 223-248, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. María Burgos & Carmen Batanero & Juan D. Godino, 2021. "Algebraization Levels in the Study of Probability," Mathematics, MDPI, vol. 10(1), pages 1-16, December.

    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. Joel B. Greenhouse & Howard J. Seltman, 2018. "On Teaching Statistical Practice: From Novice to Expert," The American Statistician, Taylor & Francis Journals, vol. 72(2), pages 147-154, April.
    2. Claudia Vásquez & Israel García-Alonso & María José Seckel & Ángel Alsina, 2021. "Education for Sustainable Development in Primary Education Textbooks—An Educational Approach from Statistical and Probabilistic Literacy," Sustainability, MDPI, vol. 13(6), pages 1-20, March.
    3. Claudia Vásquez & Ángel Alsina, 2021. "Analysing Probability Teaching Practices in Primary Education: What Tasks Do Teachers Implement?," Mathematics, MDPI, vol. 9(19), pages 1-21, October.
    4. Laura Muñiz-Rodríguez & Luis J. Rodríguez-Muñiz & Ángel Alsina, 2020. "Deficits in the Statistical and Probabilistic Literacy of Citizens: Effects in a World in Crisis," Mathematics, MDPI, vol. 8(11), pages 1-20, October.
    5. Jenna Hicks & Jessica Dewey & Yaniv Brandvain & Anita Schuchardt, 2020. "Development of the Biological Variation In Experimental Design And Analysis (BioVEDA) assessment," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-21, July.
    6. Seokmin Kang & Sungyeun Kim, 2022. "Lessons Learned from Topic Modeling Analysis of COVID-19 News to Enrich Statistics Education in Korea," Sustainability, MDPI, vol. 14(6), pages 1-16, March.
    7. Săvoiu, Gheorghe, 2008. "The scientifiv way of thinking in statistics, statistical physics and quantum mechanics," MPRA Paper 13558, University Library of Munich, Germany.
    8. Patricia Belén Carrera & Luis R. Pino-Fan & Hugo Alvarado & Jesús Guadalupe Lugo-Armenta, 2021. "Practices of the Random Variable Proposed in the Chilean Mathematics Curriculum of Secondary Education," Mathematics, MDPI, vol. 9(19), pages 1-26, October.
    9. Ricardo Ocaña-Riola, 2016. "The Use of Statistics in Health Sciences: Situation Analysis and Perspective," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 8(2), pages 204-219, October.
    10. Maria Penna & Mirian Agus & Maribel Peró-Cebollero & Joan Guàrdia-Olmos & Eliano Pessa, 2014. "The use of imagery in statistical reasoning by university undergraduate students: a preliminary study," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(1), pages 173-187, January.
    11. Anand Desai, 2008. "Quantitative methods, economics, and or models," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 27(3), pages 640-669.
    12. Joan Franco Seguí & Ángel Alsina & Claudia Vásquez, 2024. "Teaching Statistics for Sustainability across Contexts: Exploring the Knowledge and Beliefs of Teachers," Sustainability, MDPI, vol. 16(2), pages 1-22, January.
    13. Idris Djouahra, 2022. "Conceptual understanding of linear regression among economics students at the university center of Tipaza, Algeria," Croatian Review of Economic, Business and Social Statistics, Sciendo, vol. 8(2), pages 66-83, December.
    14. Gheorghe SAVOIU & Constantin MANEA, 2013. "Dimitrie Cantemir – The First Shaper of Romanian Statistical Thinking," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 61(4), pages 12-21, December.
    15. Gheorghe SAVOIU, 2012. "A Philosophical Introduction to the History of Statistics’ Conceptualization," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 60(2), pages 111-119, May.
    16. Heejoo Suh & Sohyung Kim & Seonyoung Hwang & Sunyoung Han, 2020. "Enhancing Preservice Teachers’ Key Competencies for Promoting Sustainability in a University Statistics Course," Sustainability, MDPI, vol. 12(21), pages 1-21, October.
    17. Roger W. Hoerl & Ronald D. Snee, 2017. "Statistical Engineering: An Idea Whose Time Has Come?," The American Statistician, Taylor & Francis Journals, vol. 71(3), pages 209-219, July.
    18. Theresa Büchter & Andreas Eichler & Nicole Steib & Karin Binder & Katharina Böcherer-Linder & Stefan Krauss & Markus Vogel, 2022. "How to Train Novices in Bayesian Reasoning," Mathematics, MDPI, vol. 10(9), pages 1-31, May.
    19. Katja Prevodnik & Vasja Vehovar, 2014. "Presenting dynamics of social phenomena: should we use absolute, relative or time differences?," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(2), pages 799-816, March.

    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:gam:jmathe:v:9:y:2021:i:19:p:2416-:d:645371. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.