“Value-adding” Analysis: Doing More With Qualitative Data
Author(s) -
Joan M. Eakin,
Brenda Gladstone
Publication year - 2020
Publication title -
international journal of qualitative methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.414
H-Index - 29
ISSN - 1609-4069
DOI - 10.1177/1609406920949333
Subject(s) - value (mathematics) , conceptualization , contextualization , computer science , qualitative research , epistemology , reflexivity , heuristics , generative grammar , data science , cognitive reframing , sociology , knowledge management , interpretation (philosophy) , psychology , artificial intelligence , social science , philosophy , machine learning , programming language , operating system , social psychology
Much qualitative research produces little new knowledge. We argue that this is largely due to deficits of analysis. Researchers too seldom venture beyond cataloguing data into pre-existing concepts and scouting for “themes,” and fail to exploit the distinctive powers of insight of qualitative methodology. The paper introduces a “value-adding” approach to qualitative analysis that aims to extend and enrich researchers’ analytic interpretive practices and enhance the worth of the knowledge generated. We outline key features of this form of analysis, including how it is constituted by principles of interpretation, contextualization, criticality, and the “creative presence” of the researcher. Using concrete examples from our own research, we describe some analytic “devices” that can free up and stretch a researcher’s analytic capacities, including putting reflexivity to work, treating everything as data, reading data for what is invisible, anomalous and “gestalt,” engaging in “generative” coding, deploying heuristics for theorizing, and recognizing writing as a key analytic activity. We argue that at its core, value-adding analysis is a scientific craft rather than a scientific formula, a creative assemblage of reality rather than a procedural determination of it. The researcher is the primary generative and synthesizing mechanism for transforming empirically observed data into the key products of qualitative research—concepts, accounts and explanations. The ultimate value of value-adding analysis resides in its ability to generate new knowledge, including not just the “discovery” of things heretofore unknown but also the re-conceptualization of what is already known, and, importantly, the reframing and reconstitution of the research problem.
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