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What Is Your 'Unit of Analysis' And, More Importantly, Why? New Tools And Methods for Teaching Undergraduate Social Science Students to Think about Data.

Presenter 1
Parvaneh Abbaspour
Lewis & Clark College
Presenter 2
E. J. Carter Lewis
Lewis & Clark College

The proliferation of online datasets has created myriad opportunities for undergraduate social science students to delve into complex, quantitative analysis. While students drawn to these courses are often math and statistics savvy and relatively adept at working with statistical programs, many still lack an understanding of data creation processes such as why the data were collected, how the populations delimited and sampled, and precisely how variables are defined and measured such that they might stand-in for phenomena. Moreover, the ease of acquiring these datasets can contribute to the abstraction, and more crucially the assumptions, inherent in translating the complexity of human experience into numerical values.
Common approaches to teaching undergraduate social science students to find data include referring them to the secondary literature, pointing them to data repositories, and walking them through a 'unit of analysis' worksheet. We argue that while such worksheets may help a student define the parameters of the data they are after, they reinforce the same abstraction inherent in the data dilemma to begin with. We present a range of tools developed this year to support data discovery with the goal of reinforcing data literacy for undergraduate social science students while helping them find the resources they need. These tools include the data review and the determinant inventory. We describe how we adapted and integrated these tools into a revised data discovery worksheet emphasizing a more holistic conception of how data models real world phenomena.

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