QRMine: A python package for triangulation in Grounded Theory
This work provides a tool for qualitative researchers to streamline data analysis in Grounded Theory, though it is incremental as it packages existing methods into a user-friendly interface.
The authors tackled the challenge of supporting qualitative researchers in Grounded Theory by developing QRMine, an open-source Python package that integrates machine learning and natural language processing to assist with coding and triangulation of textual and numeric data, enabling easier application of these methods.
Grounded theory (GT) is a qualitative research method for building theory grounded in data. GT uses textual and numeric data and follows various stages of coding or tagging data for sense-making, such as open coding and selective coding. Machine Learning (ML) techniques, including natural language processing (NLP), can assist the researchers in the coding process. Triangulation is the process of combining various types of data. ML can facilitate deriving insights from numerical data for corroborating findings from the textual interview transcripts. We present an open-source python package (QRMine) that encapsulates various ML and NLP libraries to support coding and triangulation in GT. QRMine enables researchers to use these methods on their data with minimal effort. Researchers can install QRMine from the python package index (PyPI) and can contribute to its development. We believe that the concept of computational triangulation will make GT relevant in the realm of big data.