Qualitative data analysis software (QDAS) has the potential to revolutionise the array of analysis techniques and the scale of qualitative research. Yet, QDAS has largely failed to facilitate methodological innovation
Qualitative data analysis software (QDAS) has the potential to revolutionise the array of analysis techniques and the scale of qualitative research. Yet, QDAS has largely failed to facilitate methodological innovation and its status and acceptance within qualitative research remains uneven. Historically, the QDAS literature was critical of software, essentialising design problems as inherent limitations. More recent contributions have challenged this, but shifted blame to poor training and user resistance. This paper makes an alternative critique by bringing together Marx’s theory of alienation and the case for free software. It sees significant limitations in extant QDAS, but views these as a product of the proprietary model they are based on. A model that centralises the means of analysis in the hands of a few private companies, locks data behind proprietary file formats, and forces researchers to adapt their analysis to the limited tools provided. By undermining community and frustrating analysis, it alienates researchers from their data, each other, and themselves. Free software restores power to communities through enshrining the freedom to use, study, share, and modify the software for any purpose. The design philosophy of PythiaQDA, a free and open source QDAS in (very) early development, will be used to illustrate the revolutionary potential of these freedoms. PythiaQDA’s vision of the future of qualitative analysis is one where everyone can access the means of analysis, modify the software to create new methodologies, work seamlessly with existing open source quantitative software, and share their analysis and findings in new creative ways.
(Tuesday) 12:00 pm - 1:00 pm
University of Glasgow
Hetherington Building, Room 133 22 Bute Gardens, Glasgow, G12 8RS
Qualitative Research at Glasgow Seminar Series