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Beyond the Interpretive: Finding Meaning in Qualitative Data

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2017 ASEE Annual Conference & Exposition


Columbus, Ohio

Publication Date

June 24, 2017

Start Date

June 24, 2017

End Date

June 28, 2017

Conference Session

Qualitative Research Methods

Tagged Division

Educational Research and Methods

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Elliot P. Douglas University of Florida

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Elliot P. Douglas is Associate Professor of Environmental Engineering Sciences and Distinguished Teaching Scholar at the University of Florida. His research interests are in the areas of active learning pedagogies, problem-solving, critical thinking, diversity in engineering, and qualitative methodologies.

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This theory paper uses two different analyses of the same qualitative data to illustrate how traditional interpretivist approaches can lead to overly simplistic interpretations. Historically, interpretivist approaches were developed in contrast to positivist quantitative analyses. The intent was to understand the contextual factors that underlie phenomena and to understand how people make meaning from their experiences. While initially the “paradigm wars” placed interpretive qualitative analysis and positivist quantitative analysis in opposition to each other, there is now greater recognition that each approach has its own strengths and weaknesses, and that different approaches are needed for answering different kinds of questions. Mixed methods are becoming more common as a means to integrate the two approaches.

Recently, some qualitative methodologists have pushed back on interpretive methods, arguing that they share important similarities with a positivist epistemology. For one, they share the characteristic of calling for removal of the researcher from the analysis process. In the positivist paradigm, researchers are supposed to be neutral in that analysis is a search for Truth. Careful attention to interrater reliability is intended to ensure that researcher bias is not introduced. In interpretivist research, analysis is supposed to be done without reference to a priori theory, and researchers are advised to “bracket” their prior experiences such that the “voices of the participants” can be heard.

Another similarity is the fragmenting of data into small elements. In quantitative research numbers are used to represent the phenomena of interest. In interpretive qualitative research coding is used to fragment and reorganize data in order to identify themes that, while perhaps contextualized in the research setting, are decontextualized with regard to an individual’s experience.

As a result of these two characteristics, interpretive analysis can lead to a fragmented, decontextualized, superficial explanation of the phenomena. Themes are often self-evident and do not provide a deep, meaningful interpretation. As an alternate approach, authors have described “post-qualitative” analysis as “thinking with theory”. The data is read through the lens of a particular theory such that meaning is interpreted simultaneously with analysis. To demonstrate the benefits of such an approach, I analyzed qualitative data using both traditional interpretive coding and “post-coding”. Data came from a single interview transcript obtained as part of a project on faculty attitudes on diversity and inclusion. Comparison of the two analyses will show how deeper meaning can be obtained through a post-qualitative analysis.

Douglas, E. P. (2017, June), Beyond the Interpretive: Finding Meaning in Qualitative Data Paper presented at 2017 ASEE Annual Conference & Exposition, Columbus, Ohio. 10.18260/1-2--27658

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