As library workers, we may want to draw insights from open civic data data through computational methods. To do this, we need to develop a question that our data can answer. In this module, we explore framing data driven questions.
How do we frame a research question that we can answer computationally?
The librarian wants to draw insights from the library WiFi usage data. They begin developing a question to ask of the data: “Is WiFi being effectively used by library patrons?” But they realize that they need to build greater specificity to the question, defining what "effective" means in the context of this analysis. They decide that one measure of service effectiveness is the number of patrons who use the Wifi. They reframe their question to be a more data driven one, asking: "Are WiFi services effective, measured by achieving the goal of annual WiFi sessions being greater than or equal to 25% of annual patron counts?”
Overview:
Look at this data activity from data basic: https://databasic.io/en/culture/ask-questions
This exercise can be done individually or in pairs.
Supplies: An Internet-connected device and paper and a pen/pencil for notetaking
Time: 20 minutes
Activity:
Select a dataset
Choose a dataset that is published on the Western Pennsylvania Regional Data Center or a data portal that is local to your region.
2. Interrogate the data
Apply these questions to your dataset and the metadata available on the data portal. Why was the data collected? Who collected the data? What tools were used to collect the data? When was the data collected? What might have been the motivations or perspectives of the individuals and/or groups collecting the data? What data wasn’t collected (or, at least, is not available to us)?
3. Share-Out
Data have context. Share what you learned about the context of your data! Note what you were unable to address, inferred, or found a clear answer to.
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