Data Literacy in the Social Sciences: Findings from a Local Study on Teaching with Quantitative Data in Undergraduate Courses
DOI:
https://doi.org/10.18438/eblip30138Abstract
Objective – The University of New Hampshire (UNH) Library conducted an exploratory study of the pedagogical practices of social science instructors at UNH who teach using quantitative data in undergraduate courses. This study is connected to a suite of parallel studies at other higher education institutions that was designed and coordinated by Ithaka S+R.
The four aims of this study were to explore the ways in which instructors teach and engage undergraduates in the social sciences using quantitative data; understand the support needs of these instructors; develop actionable recommendations for campus stakeholders; and identify opportunities for the development of resources, services, or activities in the library to support the use of quantitative data in the classroom.
Methods – For the UNH study, the research team recruited eleven participants through convenience sampling for one-on-one, semi-structured interviews. The study sample included lecturers, assistant professors, associate professors, and full professors across seven social science disciplines from the Durham and Manchester campuses.
Results – Courses using data provide a unique opportunity for students to gain experience by working with hands-on examples. The two overarching themes identified speak to both the motivations of instructors who teach with data and the challenges and opportunities they face: teaching with data for data literacy and scientific literacy and teaching with data for statistical, data, and tools skill building.
Conclusion – Data literacy is an important set of competencies in part because of the quality and quantity of data students encounter; they need to have the ability to critically evaluate data, methods, and claims. This study directed attention to an area that had not previously been examined at UNH and is an important first step toward creating greater awareness and community of practice for social science instructors teaching with data. The UNH Library offers research data services and is exploring new ways of supporting data literacy. UNH has opportunities to create additional supports for instructors and students that could improve student learning outcomes. Such efforts may require cross-college or cross-department coordination as well as administrative support.
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Brown, M. (2017). Making students part of the dataset: A model for statistical enquiry in social issues. Teaching Statistics, 39(3), 79–83. https://doi.org/10.1111/test.12131 DOI: https://doi.org/10.1111/test.12131
Cantinotti, M., Désormeaux-Moreau, M., & Balbinotti, M. (2017). Mapping psychology students’ perspective on group peer-tutoring in statistics. Mentoring & Tutoring: Partnership in Learning, 25(4), 466–484. https://doi.org/10.1080/13611267.2017.1403609 DOI: https://doi.org/10.1080/13611267.2017.1403609
Carlson, J., Fosmire, M., Miller, 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. https://doi.org/10.1353/pla.2011.0022 DOI: https://doi.org/10.1353/pla.2011.0022
Caulfield, S. L., & Persell, C. H. (2006). Teaching social science reasoning and quantitative literacy: The role of collaborative groups. Teaching Sociology, 34(1), 39–53. https://doi.org/10.1177/0092055X0603400104 DOI: https://doi.org/10.1177/0092055X0603400104
Chamberlain, J. M., Hillier, J., & Signoretta, P. (2015). Counting better? An examination of the impact of quantitative method teaching on statistical anxiety and confidence. Active Learning in Higher Education, 16(1), 51–66. https://doi.org/10.1177/1469787414558983 DOI: https://doi.org/10.1177/1469787414558983
Clark, T., & Foster, L. (2017). ‘I’m not a natural mathematician’: Inquiry-based learning, constructive alignment and introductory quantitative social science. Teaching Public Administration, 35(3), 260–279. https://doi.org/10.1177/0144739417711219 DOI: https://doi.org/10.1177/0144739417711219
Condon, P., Exline, E., & Buckley, L. (2021). Teaching with quantitative data in the social sciences at the University of New Hampshire: An Ithaka S+R local report. UNH Scholars Repository. https://dx.doi.org/10.34051/p/2021.39 DOI: https://doi.org/10.34051/p/2021.39
Condron, D. J., Becker, J. H., & Bzhetaj, L. (2018). Sources of students’ anxiety in a multidisciplinary social statistics course. Teaching Sociology, 46(4), 346–355. https://doi.org/10.1177/0092055X18780501 DOI: https://doi.org/10.1177/0092055X18780501
Cooper, D. M. (2019, July 29). Announcing two new S+R projects on supporting data work. Ithaka S+R Blog. https://sr.ithaka.org/blog/announcing-two-new-sr-projects-on-supporting-data-work/
Corbin, J., & Strauss, A. (2014). Basics of qualitative research: Techniques and procedures for developing grounded theory. Sage Publications.
Cui, S., Zhang, J., Guan, D., Zhao, X., & Si, J. (2019). Antecedents of statistics anxiety: An integrated account. Personality and Individual Differences, 144, 79–87. https://doi.org/10.1016/j.paid.2019.02.036 DOI: https://doi.org/10.1016/j.paid.2019.02.036
Elbulok-Charcape, M., Grandoit, E., Berman, L., Fogel, J., Fink, L., & Rabin, L. (2019). Improving exam performance in an undergraduate statistics course for at-risk students through peer tutoring. Psychology Teaching Review, 25(2), 3–17. DOI: https://doi.org/10.53841/bpsptr.2019.25.2.3
Faber, G., & Drexler, H. (2019). Predicting education science students’ statistics anxiety: The role of prior experiences within a framework of domain-specific motivation constructs. Higher Learning Research Communications, 9(1). https://eric.ed.gov/?id=EJ1221253 DOI: https://doi.org/10.18870/hlrc.v9i1.435
Fellers, P. S., & Kuiper, S. (2020). Introducing undergraduates to concepts of survey data analysis. Journal of Statistics Education, 28(1), 18–24. https://doi.org/10.1080/10691898.2020.1720552 DOI: https://doi.org/10.1080/10691898.2020.1720552
Filiz, M., Early, E., Thurston, A., & Miller, S. (2020). Measuring and improving university students’ statistics self-concept: A systematic review. International Journal of Educational Research Open, 1, Article 100020. https://doi.org/10.1016/j.ijedro.2020.100020 DOI: https://doi.org/10.1016/j.ijedro.2020.100020
Hartnett, J. L. (2016). Using data mining to teach applied statistics and correlation. Teaching of Psychology, 43(2), 142–146. https://doi.org/10.1177/0098628316636292 DOI: https://doi.org/10.1177/0098628316636292
Hilal, S. M., & Redlin, M. M. (Eds.). (2004). Integrating data analysis: Instructional materials for integrating data analysis into sociology courses. American Sociological Association.
Hosein, A., & Rao, N. (2019). The acculturation and engagement of undergraduate students in scientific thinking through research methods. In M. Murtonen & K. Balloo (Eds.), Redefining scientific thinking for higher education: Higher-order thinking, evidence-based reasoning and research skills (pp. 157–175). Springer International Publishing. https://doi.org/10.1007/978-3-030-24215-2_7 DOI: https://doi.org/10.1007/978-3-030-24215-2_7
Intepe, G., & Shearman, D. (2020). Developing statistical understanding and overcoming anxiety via drop-in consultations. Statistics Education Research Journal, 19(1), 149–166. https://iase-web.org/ojs/SERJ/article/download/127/35 DOI: https://doi.org/10.52041/serj.v19i1.127
Ithaka S+R. (2020). Teaching with data in the social sciences ethics review instructions. [Unpublished internal documentation].
Lawrence, R. (2004). Teaching data structures using competitive games. IEEE Transactions on Education, 47(4), 459–466. https://doi.org/10.1109/TE.2004.825053 DOI: https://doi.org/10.1109/TE.2004.825053
Lovekamp, W. E., Soboroff, S. D., & Gillespie, M. D. (2017). Engaging students in survey research projects across research methods and statistics courses. Teaching Sociology, 45(1), 65–72. https://doi.org/10.1177/0092055X16673136 DOI: https://doi.org/10.1177/0092055X16673136
MacArthur, K. R. (2020). Avoiding over-diagnosis: Exploring the role of gender in changes over time in statistics anxiety and attitudes. Numeracy: Advancing Education in Quantitative Literacy, 13(1), 1–26. https://doi.org/10.5038/1936-4660.13.1.4 DOI: https://doi.org/10.5038/1936-4660.13.1.4
Neumann, D. L., Hood, M., & Neumann, M. M. (2013). Using real-life data when teaching statistics: Student perceptions of this strategy in an introductory statistics course. Statistics Education Research Journal, 12(2), 59–70. https://doi.org/10.52041/serj.v12i2.304 DOI: https://doi.org/10.52041/serj.v12i2.304
Nurse, A. M., & Staiger, T. (2019). Teaching data reproducibility through service learning. Teaching Sociology, 47(4), 350–357. https://doi.org/10.1177/0092055X19860577 DOI: https://doi.org/10.1177/0092055X19860577
Ralston, K., MacInnes, J., Crow, G., & Gayle, V. J. (2016). We need to talk about statistical anxiety. A review of the evidence around statistical anxiety in the context of quantitative methods pedagogy [Working paper]. NCRM. https://eprints.ncrm.ac.uk/id/eprint/3987/
Rode, J. B., & Ringel, M. M. (2019). Statistical software output in the classroom: A comparison of R and SPSS. Teaching of Psychology, 46(4), 319–327. https://doi.org/10.1177/0098628319872605 DOI: https://doi.org/10.1177/0098628319872605
Ruediger, D., Cooper, D. M., Bardeen, A., Baum, L., Ben-Gad, S., Bennett, S., Berger, K., Bonella, L., Brazell, R., Bruce, S., Buckley, L., Burrows, T., Calvert, S., Condon, P., Curty, R., Davis, H., Exline, E., Feerrar, J., Finch, E., . . . Yatcilla, J. (2022). Fostering data literacy: Teaching with quantitative data in the social sciences. Ithaka S+R. https://doi.org/10.18665/sr.317506 DOI: https://doi.org/10.18665/sr.317506
Wolff, A., Gooch, D., Montaner, J. J. C., Rashid, U., & Kortuem, G. (2016). Creating an understanding of data literacy for a data-driven society. The Journal of Community Informatics, 12(3), pp. 9–26. https://doi.org/10.15353/joci.v12i3.3275 DOI: https://doi.org/10.15353/joci.v12i3.3275
Wollschleger, J. (2019). Making it count: Using real-world projects for course assignments. Teaching Sociology, 47(4), 314–324. https://doi.org/10.1177/0092055X19864422 DOI: https://doi.org/10.1177/0092055X19864422
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