Conceptions of data literacy in the statistics education literature
DOI:
https://doi.org/10.29173/iq1156Keywords:
data literacy, statistics education, reproducibility, data discoveryAbstract
Data literacy is an increasingly important skill in our data-driven world, and librarians and other information professionals can play a key role in creating a data literate population due to data literacy’s close association with information literacy. However, the definition of data literacy and the attention paid to certain competencies varies greatly between fields: what librarians and statisticians mean by “data literacy” is not the same thing. A scoping review of data literacy articles within the field of statistics education reveals the landscape of data literacy education in statistics, giving librarians and other information professionals a map for coordinating their data literacy work with disciplinary faculty. The areas of data discovery, evaluating and ensuring the quality of data and its sources, and reproducibility are closely examined. These areas are defined and valued inconsistently amongst information professionals and statisticians, but their close associations to traditional library services creates an ideal opportunity for libraries and data archives to contribute to data literacy education.
References
Ben-Zvi, D., & Garfield, J. (Eds.). (2004). The Challenge of Developing Statistical Literacy, Reasoning and Thinking. Springer Netherlands. https://doi.org/10.1007/1-4020-2278-6
Bertino, E. (2015). Data trustworthiness—approaches and research challenges. In Data Privacy Management, Autonomous Spontaneous Security, and Security Assurance (Vol. 8872, pp. 17–25). Springer International Publishing AG. https://doi.org/10.1007/978-3-319-17016-9_2
Bilgin, A. A. B., Powell, A., & Richards, D. (2022). Work integrated learning in data science and a proposed assessment framework. Statistics Education Research Journal, 21(2), 1-. https://doi.org/10.52041/serj.v21i2.26
Bonikowska, A., Sanmartin, C., & Frenette, M. (2019, August 14). Data literacy: what is it and how to measure it in the public service. https://www150.statcan.gc.ca/n1/en/pub/11-633-x/11-633-x2019003-eng.pdf
Brungard, A., & Smith, L. (2021). A data discovery project: seeking truth in a post-truth world. In J. Bauder (Eds.), Data Literacy in Academic Libraries. American Library Association.
Carlson, J., Fosmire, M., Miller, C. C., & Nelson, M. S. (2011). Determining data information literacy needs: A study of students and research faculty. portal: Libraries and the Academy, 11(2), 629-657. http://dx.doi.org/10.1353/pla.2011.0022
Caballer-Tarazona, M., & Coll-Serrano, V. (2020). The raising factor, that great unknown. A guided activity for undergraduate students. Journal of Statistics Education, 28(3), 304–315. https://doi.org/10.1080/10691898.2020.1832006
Casleton, E., Beyler, A., Genschel, U., & Wilson, A. (2014). A pilot study teaching metrology in an introductory statistics course. Journal of Statistics Education, 22(3), 1. https://doi.org/10.1080/10691898.2014.11889710
Çetinkaya-Rundel, M., Dogucu, M., & Rummerfield, W. (2022). The 5ws and 1h of term projects in the introductory data science classroom. Statistics Education Research Journal, 21(2), 1–19. https://doi.org/10.52041/serj.v21i2.37
Chicco, D., Fabris, A., & Jurman, G. (2025). The Venus score for the assessment of the quality and trustworthiness of biomedical datasets. BioData Mining, 18(1), 1–31. https://doi.org/10.1186/s13040-024-00412-x
Consortium for the Advancement of Undergraduate Statistics Education (CAUSE) (n.d.). Journals publishing research in statistics education. https://www.causeweb.org/cause/research/journals
Curley, B., & Peterson, A. (2022). A fresh shot at statistics in the classroom: three perspectives using world cup soccer player data. Journal of Statistics and Data Science Education, 30(1), 86–98. https://doi.org/10.1080/26939169.2021.2008283
Dangol, A., & Dasgupta, S. (2023). Constructionist approaches to critical data literacy: A review. Proceedings of the 22nd Annual ACM Interaction Design and Children Conference, 112–123. https://doi.org/10.1145/3585088.3589367
Delport, D. H. (2023). The development of statistical literacy among students: Analyzing messages in media articles with Gal’s worry questions. Teaching Statistics, 45(2), 61–68. https://doi.org/10.1111/test.12308
Donoghue, T., Voytek, B., & Ellis, S. E. (2021). Teaching creative and practical data science at scale. Journal of Statistics and Data Science Education, 29(S1), S27–S39. https://doi.org/10.1080/10691898.2020.1860725
Downes, S. (2023). Three frameworks for data literacy. In D. G. Sampson, D. Ifenthaler, D., & P. Isaías (Eds.), Proceedings of the 20th International Conference on Cognition and Exploratory Learning in the Digital Age (107-115). IADIS Press. https://files.eric.ed.gov/fulltext/ED636095.pdf
Ferris, M., & Cheng, S. (2018). Using twitter to energize the introductory statistics class. Technology Innovations in Statistics Education, 11(1). https://doi.org/10.5070/T5111032036
Fleischer, Y., Biehler, R., & Schulte, C. (2022). Teaching and learning data-driven machine learning with educationally designed jupyter notebooks. Statistics Education Research Journal, 21(2), 1–25. https://doi.org/10.52041/serj.v21i2.61
Frölich, N., & Schellhammer, K. S. (2022). Questionnaire design and sampling procedures for business and economics students: A research-oriented, hands-on course. International Journal of Mathematical Education in Science and Technology, 0(0), 1–19. https://doi.org/10.1080/0020739X.2022.2056722
Giarlo, M. J. (2013). Academic libraries as data quality hubs. Journal of Librarianship and Scholarly Communication, 1(3). https://doi.org/10.7710/2162-3309.1059
Gregory, K., Khalsa, S. J., Michener, W. K., Psomopoulos, F. E., de Waard, A., & Wu, M. (2018). Eleven quick tips for finding research data. PLoS Computational Biology, 14(4). https://doi.org/10.1371/journal.pcbi.1006038
Hassad, R. A. (2020). A Foundation for Inductive Reasoning in Harnessing the Potential of Big Data. Statistics Education Research Journal, 19(1), 238–258. https://doi.org/10.52041/serj.v19i1.133
Huck, J. (2020). Identifying, accessing and evaluating data: finding and accessing data can be problematic, but many of the skills used in traditional reference can be applied to data discovery. Information Outlook, 24(1), 4-6. https://scholarworks.sjsu.edu/sla_io_2020/1
ISO/IEC. (2008). Software Engineering—Software Product Quality Requirements and Evaluation (SQuaRE)—Data Quality Model (25012:2008). https://www.iso.org/standard/35736.html
Jones, J. D. (2022). Using school mathematics to develop students’ data literacy skills. Mathematics Teacher: Learning and Teaching PK-12, 115(8), 576–581. https://doi.org/10.5951/MTLT.2021.0239
Koedel, U., Schuetze, C., Fischer, P., Bussmann, I., Sauer, P. K., Nixdorf, E., Kalbacher, T., Wichert, V., Rechid, D., Bouwer, L. M., & Dietrich, P. (2022). Challenges in the evaluation of observational data trustworthiness from a data producers viewpoint (FAIR+). Frontiers in Environmental Science, 9. https://doi.org/10.3389/fenvs.2021.772666
Koga, S. (2022). Characteristics of statistical literacy skills from the perspective of critical thinking. Teaching Statistics, 44(2), 59–67. https://doi.org/10.1111/test.12302
Korstjens, I., & Moser, A. (2018). Series: Practical guidance to qualitative research. Part 4: Trustworthiness and publishing. The European Journal of General Practice, 24(1), 120–124. https://doi.org/10.1080/13814788.2017.1375092
Lee, H. S., Mojica, G. F., Thrasher, E. P., & Baumgartner, P. (2022). Investigating data like a data scientist: key practices and processes. Statistics Education Research Journal, 21(2), 1–23. https://doi.org/10.52041/serj.v21i2.41
Logan, J., Webb, J., Singh, N. K., Tanner, N., Barrett, K., Wall, M., Walsh, B., & Ayala, A. P. (2024). Scoping review search practices in the social sciences: A scoping review. Research Synthesis Methods, 15(6), 950–963. https://doi.org/10.1002/jrsm.1742
Mahanti, R. (2019). Data Quality: Dimensions, Measurement, Strategy, Management, and Governance. Quality Press. http://ebookcentral.proquest.com/lib/grinnell-ebooks/detail.action?docID=6262212
Maggio, L. A., Larsen, K., Thomas, A., Costello, J. A., & Artino Jr., A. R. (2021). Scoping reviews in medical education: A scoping review. Medical Education, 55(6), 689–700. https://doi.org/10.1111/medu.14431
Mathiak, B., Juty, N., Bardi, A., Colomb, J., & Kraker, P. (2023). What are researchers’ needs in data discovery? Analysis and ranking of a large-scale collection of crowdsourced use cases. Data Science Journal, 22(1). https://doi.org/10.5334/dsj-2023-003
McNamara, A. (2019). Key attributes of a modern statistical computing tool. The American Statistician, 73(4), 375–384. https://doi.org/10.1080/00031305.2018.1482784
Medeiros, P., Shetty, J., Lamaj, L., Cunningham, J., Wanigaratne, S., Guttmann, A., & Cohen, E. (2024). Reported community engagement in health equity research published in high-impact medical journals: A scoping review. https://doi.org/10.1136/bmjopen-2024-084952
Million, A. J., York, J., Lafia, S., & Hemphill, L. (2024). Data, not documents: Moving beyond theories of information‐seeking behavior to advance data discovery. Journal of the Association for Information Science and Technology. https://doi.org/10.1002/asi.24962
Plesser, H.E. (2018). Reproducibility vs. replicability: A brief history of a confused terminology. Frontiers in Neuroinformatics 11 (76). https://doi.org/10.3389/fninf.2017.00076
Prado, J.C., & Marzal, M.A. (2013). Incorporating data literacy into information literacy programs: Core competencies and contents. Libri 63 (2): 123–134. https://doi.org/10.1515/libri-2013-0010
Roth, W.-M., & Temple, S. (2014). On understanding variability in data: A study of graph interpretation in an advanced experimental biology laboratory. Educational Studies in Mathematics, 86(3), 359–376. https://doi.org/10.1007/s10649-014-9535-5
Schield, M. (2004). Information literacy, statistical literacy and data literacy. IASSIST Quarterly Summer/Fall: 6–11. https://doi.org/10.29173/iq790
Sun, G., Friedrich, T., Gregory, K., & Mathiak, B. (2024). Supporting data discovery: comparing perspectives of support specialists and researchers. Data Science Journal, 23(1). https://doi.org/10.5334/dsj-2024-048
Towse, J., Davies, R., Ball, E., James, R., Gooding, B., & Ivory, M. (2022). LUSTRE: an online data management and student project resource. Journal of Statistics and Data Science Education, 30(3), 266–273. https://doi.org/10.1080/26939169.2022.2118645
Wilkerson, M. H., Lanouette, K., & Shareff, R. L. (2022). Exploring variability during data preparation: A way to connect data, chance, and context when working with complex public datasets. Mathematical Thinking and Learning, 24(4), 312–330. https://doi.org/10.1080/10986065.2021.1922838
Wilson, M., Ross, A., & Casey, S. (2021). A classroom‐ready activity on educational disparities in the United States. Teaching Statistics, 43(S1), S93–S97. https://doi.org/10.1111/test.12252
Zhu, Y., Hernandez, L. M., Mueller, P., Dong, Y., & Forman, M. R. (2013). Data acquisition and preprocessing in studies on humans: what is not taught in statistics classes? The American Statistician, 67(4), 235–241. https://doi.org/10.1080/00031305.2013.842498
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Julia Bauder, Libby Cave
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
This license lets others remix, tweak, and build upon your work non-commercially, and although their new works must also acknowledge you and be non-commercial, they don’t have to license their derivative works on the same terms.
The Creative Commons-Attribution-Noncommercial License 4.0 International applies to all works published by IASSIST Quarterly. Authors will retain copyright of the work. Your contribution will be available at the IASSIST Quarterly website when announced on the IASSIST list server.
