"SHARE: A Framework for Secondary Qualitative Data Analysis" by Shawn Jordan, Holly Matusovich et al.
 

Document Type

Article

Publication Date

1-1-2024

Journal Title

Studies in Engineering Education

Volume Number

5

Issue Number

1

First Page

125

Last Page

133

DOI

https://doi.org/10.21061/see.175

Version

Publisher PDF: the final published version of the article, with professional formatting and typesetting

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a CC BY License.

Disciplines

Engineering

Abstract

Although secondary analysis of qualitative data is not a new research approach, it is not yet commonly used in engineering education research. Heaton (2008, p. 34) describes secondary qualitative data analysis (SDA) as involving “the re-use of pre-existing qualitative data derived from previous research studies.” Within this broad definition, one form of SDA that is especially uncommon in engineering education research (EER) is the practice in which researchers who were not part of the original project team work with qualitative data they did not help collect and potentially even apply theories and analytic lenses that were not part of the original research plan. Because these new researchers have different relationships with the data, ensuring quality in this form of SDA requires a relational approach to conducting research that links those we term data originators and those we term secondary analysts. Toward this end, we have recently completed a project in which we sought to engage the EER community on the potential affordances of SDA in qualitative research, to understand the reasons why this approach remains relatively rare in our field, and to use our experiences with SDA to propose practices and principles that can inform this approach moving forward.1 One outcome of that project is the relational approach detailed in this editorial.

We advocate for SDA because the advantages of shared approaches to leveraging existing datasets can include (1) reduced time to publication (particularly for graduate students), (2) reduced load on participants (particularly those from populations marginalized in engineering, who may receive numerous requests to participate in research studies), (3) maximized use of data collected with public funds, and (4) greater equity in the field through data transparency. In spite of these advantages, challenging questions remain—most notably, how to legally and ethically conduct SDA without sacrificing quality or harming participants, and how to conduct robust high-quality analyses when data were collected for another purpose. Although challenges remain, we argue that it is possible to make advances on these questions. Thus, here we offer practical guidance for engineering education researchers conducting SDA, as well as address some frequently asked questions about SDA that can arise.

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