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Integrating Visual Analytics Support for Grounded Theory Practice in Qualitative Text Analysis
Author(s) -
Chandrasegaran Senthil,
Badam Sriram Karthik,
Kisselburgh Lorraine,
Ramani Karthik,
Elmqvist Niklas
Publication year - 2017
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.13180
Subject(s) - grounded theory , visual analytics , computer science , interactive visual analysis , cultural analytics , data science , analytics , data analysis , data visualization , visualization , human–computer interaction , process (computing) , qualitative research , artificial intelligence , data mining , semantic analytics , social science , semantic web , semantic web stack , sociology , operating system
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.