Keeping Up with… Statistical Literacy

This edition of Keeping Up With...was written by Lindsay Davis and Lynda Kellam.

Lindsay Davis is an Instruction and Outreach Librarian at the University of California Merced, email: ldavis23@ucmerced.edu. Lynda Kellam is the Data Services and Government Information Librarian and Assistant Director of International & Global Studies at the University of North Carolina at Greensboro, email: lmkellam@uncg.edu.

What is Statistical Literacy?

The ability to read and interpret numeric information as presented in tables, graphs, and text goes by many names. Some use the term “quantitative literacy” or “quantitative reasoning” while others use the term “statistical literacy.” Milo Schield, a pioneer in statistical literacy education and professor at Augsburg College in Minneapolis, Minnesota, defines statistical literacy as the study of “the use of statistics as evidence in arguments.” [1] A commonly used related term is data literacy. This literacy concentrates on ”how to obtain and manipulate data,” typically in the form of a dataset or data study, as opposed to the interpretation of aggregate statistics, such as displayed in a table or graph. [2] While statistical literacy and data literacy are connected, they represent two distinct skill sets. Statistical literacy requires the ability to interpret aggregate statistics and is a baseline skill for understanding and navigating information, while data literacy requires the ability to manipulate datasets with the goal of performing data analysis. Both literacies are connected to information literacy, which is the ability to "recognize when information is needed and … the ability to locate, evaluate, and use effectively the needed information.” [3]

What is the Librarian’s Role?

During reference transactions and on library research guides, librarians frequently direct students to information that can be found in statistical sources, from government websites to subscription-based numeric databases. Often, however, students are left on their own to interpret statistics. While librarians are not typically trained to teach statistical analysis, we are expected to teach students how to evaluate information and can assist students by drawing their attention to major evaluative concerns. [4] We can encourage students to ask questions [5], such as the following:

  • Who? Who is the creator of the statistics? What is the purpose for creating this information?
  • What? What indicators are most interesting? What population is most relevant? What figure is being displayed (i.e., is it a percentage, an average, an index number?)
  • When? When were the statistics compiled? What time frame do the statistics cover? What is the periodicity?
  • Where? Which geographies are most appropriate to help answer the research question? Do students need state level or county level statistics? Which levels are realistically available?
  • How? How can the statistics be accessed? Does the library have the resources on hand? Do the statistics need to be accessed from another library or website?

These questions are, of course, similar to the types of questions needed to evaluate textual material, but consider the unique characteristics of statistical information.

How Can Librarians Incorporate Statistical Literacy into Instruction?

Though little has been written about statistical literacy in relation to information literacy instruction, studies by Katharin Peter and Lynda Kellam, Adam Beauchamp and Christine Murray, and Joanna M. Burkhardt show how librarians can easily incorporate statistical literacy evaluation activities into their classroom sessions. [6] For example, a simple activity would involve the evaluation of the information presented in a graph or a table using the questions listed above. This could be built into existing evaluation activities for textual information, such as those based on CRAAP, ABC, BEAM, etc. tests, and would not require much additional time in the classroom.

Using statistical information in class discussions, readings, and activities can also help further understanding of several of the frames in the Framework for Information Literacy for Higher Education, including Authority is Constructed and Contextual, Information Creation as a Process, Information Has Value, and Research as Inquiry. [7] For example, librarians could use a post from the blog Junk Charts to talk through the choices an author must make in creating an infographic [8]. Librarians could then lead a discussion about why an author made specific choices or decided to visualize information and statistics in a particular way.

Conclusion

With increasing access to data and statistics, statistical literacy is a critical skill for functioning in everyday life. Statistical literacy should be promoted through a concerted and coordinated campus-wide effort that includes quantitative reasoning as part of the curriculum. Nevertheless, librarians are logical partners to promote and to support these efforts. The ability to interpret and to understand information no matter the format is a fundamental skill that librarians can model, develop, and support through our information literacy sessions and research consultations.

Definitions & Importance of Statistical Literacy

Ben-Zvi, David and Garfield, Joan (Eds.). The Challenge of Developing Statistical Literacy, Reasoning, and Thinking. Dordrecht: Kluwer, 2004.

Gal, Iddo. "Adults’ statistical literacy: Meanings, components, and responsibilities." International Statistical Review, 70:1 (2002), 1-25. http://dx.doi.org/10.2307/1403713

Garfield, Joan and Ben-Zvi, David (Eds.). Developing Students' Statistical Reasoning: Connecting Research and Teaching Practice. Dordrecht: Springer, 2008.

Gray, Ann. "Data and Statistical Literacy for Librarians." IASSIST Quarterly, 28: 2-3 (2004), 24-9. http://www.iassistdata.org/iq/issue/28/2

Schield, Milo. Statistical Literacy Curriculum Design, International Association for Statistics Education Roundtable Conference, Lund, Sweden, June 28–July 3, 2004. Voorburg, The Netherlands: International Statistical Institute. Accessed May 2, 2017 https://iase-web.org/documents/papers/rt2004/2.4_Schield.pdf

Utts, Jessica. "What Educated Citizens Should Know about Statistics and Probability." The American Statistician, 57: 2 (2003), 74-9. http://dx.doi.org/10.1198/0003130031630

Wallman, Katherine K. "Enhancing Statistical Literacy: Enriching Our Society". Journal of the American Statistical Association, 88: 421 (1993), 1-8. http://dx.doi.org/10.1080/01621459.1993.10594283

Incorporating Statistical Literacy into the Information Literacy Curriculum

Beauchamp, Adam and Murray, Christine. "Teaching Foundational Data Skills in the Library." In L. Kellam and K. Thompson (Eds.), Databrarianship: The Academic Data Librarian in Theory and Practice (pp. 81-92). Chicago: Association of College and Research Libraries, 2016.

Burkhardt, Joanna M. "Information Creation as a Process." In Teaching Information Literacy Reframed: 50+ Framework-Based Exercises for Creating Information-Literate Learners (pp. 75-94). Chicago, IL: ALA Neal-Schuman, 2016.

  • Exercise 36 on p. 90 includes a statistics exercise.

Partlo, Kristin. “The Pedagogical Data Reference Interview.” IASSIST Quarterly, 33:4 & 34:1 (Spring 2010), 6-10.

  • Partlo briefly discusses the quantitative reasoning initiatives at Carleton College and the role of the library in those initiatives.

Peter, Katharin and Kellam, Lynda. Data on the Run. Workshop guide for the ACRL 2013 National Conference.

Peter, Katharin and Kellam, Lynda. “Statistics & The Single Girl: Incorporating Statistical Literacy into Information Literacy Instruction.” LOEX Quarterly, 40: 1 (2013), 2-3, 10.

  • The student learning outcomes here are based on the now rescinded Information Literacy Competency Standards for Higher Education but can be adapted to fit the Framework for Information LIteracy for Higher Education.

Notes

[1] Milo Schield. “Information Literacy, Statistical Literacy, and Data Literacy.” IASSIST Quarterly, 28: 3 (2004): 6-11.

[2] Ibid.

[3] American Library Association, Presidential Committee on Information Literacy: Final Report, (1989). Accessed May 1, 2017.

[4] Katharin Peter and Lynda Kellam, “Statistics & The Single Girl: Incorporating Statistical Literacy into Information Literacy Instruction,LOEX Quarterly, 40: 1 (2013): 2-3, 10.

[5] Lynda Kellam, Data Librarianship. Accessed May 31, 2017; Adapted from Katharin Peter and Lynda Kellam, Data on the Run, workshop guide for the ACRL 2013 National Conference.

[6] Katharin Peter and Lynda Kellam, “Statistics & The Single Girl”; Adam Beauchamp and Christine Murray, “Teaching Foundational Data Skills in the Library,” in Lynda Kellam and Kristi Thompson (Eds.), Databrarianship: The Academic Data Librarian in Theory and Practice (pp. 81-92). Chicago: Association of College and Research Libraries, 2015; Joanna M. Burkhardt, “Information Creation as a Process,” In Teaching Information Literacy Reframed: 50+ Framework-Based Exercises for Creating Information-Literate Learners (pp. 75-94). Chicago, IL: ALA Neal-Schuman, 2016.

[7] Association of College and Research Libraries, “Framework for Information Literacy for Higher Education,” 2016, Accessed May 30, 2017.

[8] Junk Charts was created by Kaiser Fung and is one of the most well known data visualization blogs. Fung critiques charts and graphs from popular media and explains common issues in data viz. While his discussions sometimes assume a high level of knowledge, he has detailed explanations as well as links to more information.