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Information & Data Literacy: Data Literacy

This guide will help you become more information and data literate.

Data Literacy

Data is qualitative or quantitative values that correlate to pieces of information. You can collect, measure, analyze, manipulate, and share data to understand information. Watch this video from the University of Guelph Libraries to learn more about the types of data and when we use data.

Qualitative vs. Quantitative Data

Data literacy is the ability to find, evaluate, and use data. Becoming data literate will help you understand how, when, why, and which data to use. There are 5 skills that a data literate person should learn:

5 Skills of a Data Literate Person

There are multiple places that you can look at to find datasets and statistics:

  • Search our Data & Datasets tab on the Open Educational Resources Resource Guide to find open access data repositories.
  • Identify specific organizations, academic institutions, and government agencies that produce data in your subject.
  • Look in professional literature for references to statistics and data.
    • Look through University of Michigan’s ICPSR Data Archive to find research literature in social and behavioral sciences.

Evaluating data sources will help you understand whether the data is relevant to your research and whether it is credible for use. To determine relevance, ask yourself these questions:

  • Was the data collected recently?
  • Is the data cross-sectional or longitudinal? Is this appropriate for your research?
  • Are the research subjects representative of what you are researching?
  • Was the data analysis done at the right level for your research? Can you logically make inferences to help your argument?

Understanding any assumptions and biases involved in the creation of data frames whether the data is usable and trustworthy. Ask yourself these questions to decide whether data is usable:

  • What are the potential sources of bias?
  • What is the method of data collection?
  • What is the strongest argument for using the data?
  • What is the strongest argument against using the data?