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Author
Jaeger, E.L.Affiliation
Department of Teaching, Learning, and Sociocultural Studies, College of Education, University of ArizonaIssue Date
2022-11-29
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SAGE Publications Inc.Citation
Jaeger, E. L. (2022). Theory-Responsive Data Analysis: Searching for the Closest Fit. International Journal of Qualitative Methods, 21.Rights
© The Author(s) 2022. This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/).Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
Qualitative researchers differ in their beliefs about the role of theory in data analysis. Some begin with theoretical constructs employed as codes in a deductive process while others hold theory at bay until late in an inductive process (Gibbs, 2007). Still others toggle back and forth from theory to data to theory and so on. Researchers have offered comprehensive accounts of inductive coding (e.g., grounded theory; Charmaz, 2006) or general descriptions of the application of theory within data analysis (Anfara & Mertz, 2006; Dressman, 2008). Absent from the literature, however, is a detailed description of a back-and-forth analysis practice. The current article fills this gap in the literature by presenting a specific procedure in which the researcher begins with an initial theoretical position and then examines the data to establish a key question raised by that data. The process continues as the researcher considers a variety of theories which address the question, selects the “best fit” theory, carefully applies that theory to the data and supporting literature, and finally reflects on ways in which study findings may enhance the theory. I exemplify this activity with data from a study of members of a writing group for adults with major mental illness. © The Author(s) 2022.Note
Open access journalISSN
1609-4069Version
Final published versionae974a485f413a2113503eed53cd6c53
10.1177/16094069221143000
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Except where otherwise noted, this item's license is described as © The Author(s) 2022. This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/).