Integrating Visual Analytics Support for Grounded Theory Practice in Qualitative Text Analysis

Journal article (2017)

Authors

R.S.K. Chandrasegaran College of Information Studies

Sriram Karthik Badam University of Maryland

Lorraine Kisselburgh Purdue University

Karthik Ramani Purdue University

Niklas Elmqvist College of Information Studies, University of Maryland

Affiliation

External organisation

Categories and Subject Descriptors (according to ACM CCS) H.5.2 [Computer Graphics]: Information Interfaces and Presentation—User Interfaces—Interaction styles

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Published Date

2017-6

Language

English

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Abstract

We present an argument for using visual analytics to aid Grounded Theory methodologies in qualitative data analysis. Grounded theory methods involve the inductive analysis of data to generate novel insights and theoretical constructs. Making sense of unstructured text data is uniquely suited for visual analytics. Using natural language processing techniques such as parts-of-speech tagging, retrieving information content, and topic modeling, different parts of the data can be structured and semantically associated, and interactively explored, thereby providing conceptual depth to the guided discovery process. We review grounded theory methods and identify processes that can be enhanced through visual analytic techniques. Next, we develop an interface for qualitative text analysis, and evaluate our design with qualitative research practitioners who analyze texts with and without visual analytics support. The results of our study suggest how visual analytics can be incorporated into qualitative data analysis tools, and the analytic and interpretive benefits that can result.