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Critical Digital Literacies: Data Literacy

Reading for Data Literacy

Data Literacy at a Glance

What is Data?

Icon of a line graphQuantitative data focuses on numbers and numeric data, including raw numbers, percentages, percentiles, and averages (mean, median, mode). Quantitative data tends to be objective in nature and numbers support research outcomes. Analysis of quantitative data involves statistical techniques and the type of data collected guides the analysis process. 

 

Icon of Qualitative Research materials Qualitative data includes text, words, ideas, and observed behaviors. In general, those working with qualitative data attempt to describe and interpret human behavior based primarily on the words and actions of selected individuals. Qualitative data tends to be subjective in nature and uses information derived from experiences to support research findings. The analysis of qualitative data can come in many forms including highlighting key words, extracting themes or behaviors, and elaborating on concepts.

 

Icon Representing Big DataBig Data is a term used to refer to datasets that are too large or complex for traditional computational methods. As with smaller-scale data, big data analysis includes ingesting, storing, cleaning, analyzing, and visualizing results. However, it is often performed by high performance computing, also know as cluster computing. 

When thinking about Big Data, keep in mind the four V's:

  • Volume - data on a large scale, typically much larger than an average dataset
  • Velocity - speed at which data moves through a system
  • Variety - the range of sources producing data
  • Veracity - the quality and accuracy of big data

Icons via the Noun Project


THE DATA LIFECYCLE

Data that is encountered on a daily basis comes from all stages of the Data Lifecycle in the form of documents and spreadsheets, laboratory notes, questionnaires, videos, source code, metadata, databases & database content, models, algorithms, weather and news facts and figures. 

The Data Lifecycle provides a high level overview of management and preservation for the use and reuse of data. It also provides the junctures at which data is likely to be encountered. Knowing where in the lifecycle data occurs, can tell about the data, its security needs, and places where it is at risk for alterations, manipulation, and bias:

What is Data Literacy?

Data literacy can be defined as the component of information literacy that enables individuals to access, interpret, critically assess, manage, handle and ethically use data. 

Data Literacy includes: 

  • Being a critical consumer of data, statistics, and visualizations
  • Understanding the content and context of data, statistics, and visualizations in news, social media, academic articles, magazines, and on the internet
  • Asking Who? What? When? Where? and Why? when searching for, gathering, and evaluating data resources and datasets
  • Understanding and recognizing bias in data
  • Understanding that data points can be distributed in different ways
  • knowing devices such as computers, cell phones, sensors, and exercise trackers produce data

Data Resources

Data and Bias

When encountering or working with data, understanding the origins, assumptions, and methods involved in its creation will help frame whether or not it is usable and trustworthy. Some good questions to ask before making any decisions with data are:

  • What are the potential sources of bias in this data?
  • What is the method of data collection within the research study?
  • What is the strongest argument for using this data?
  • What is the strongest argument against using this data?

Skill Building: Data Literary in Action

Statistical Literacy: Critically "reading," conceptualizing, and interpreting raw and synthesized data 
Data Visualization: Creating and comprehending mapped data, infographics, graphs, and charts
Data in Argument: Knowing how data is used informationally and persuasively to support arguments 
Big Data and Citizen Science: Recognizing and advocating for big data and the greater good
Personal Data Management: Knowing that clicks matter and how to protect personal data
Ethical Data Use: Using data accurately and citing sources

Adapted from:

Fontichiaro, K., & J.A. Oehrli (2016), Why Data Literacy Matters, Knowledge Quest, 44 5

Sources

Fontichiaro, K., & J.A. Oehrli. (2016). Why Data Literacy Matters, Knowledge Quest, 44(5), 21-27. 

Koltay, T. (2017). "Data Literacy for Researchers and Data Librarians," Journal of Librarianship and Information Science, 49(1), 3-14.

Prado, J.C., & M.A. Marzal. (2013). "Incorporating Data Literacy into Information Literacy Programs: Core Competencies and Contents," Libri 63(2), 123-134. 

Schield, M. (2004). "Information Literacy, Statistical Literacy, and Data Literacy," IASSIST Summer/Fall, 6-11. 

Steeves, V. (2018, September) Spatial, quant, qualitative, and 'big' data. Retrieved from https://vickysteeves.gitlab.io/lis-628-datalibrarianship/sqqb-data.html.

Wolf, A. et, all. (2016). "Creating an Understanding of Data Literacy for a Data-dviven Society," The Journal of Community Informatics, 12(3), 9-26.