THE END OF TRADITIONAL FOCUS GROUPS? : SCALING UP QUALITATIVE RESEARCH QUICK, YET MAINTAINING DEPTH OF DATA WITH LARGER SAMPLES

Abstract

The key benefits of qualitative research are rich insights and thick data. However, some argue these come at the cost of small sample sizes and low generalisability of findings. With traditional FGDs (FGDs), this could be addressed by conducting multiple groups. However, this requires significant investment of time and manpower. We explored methods to gather thick data quickly, aiming to increase the number of respondents without increasing manpower or lengthening fieldwork while maintaining data quality. In this paper, we detail our experience running a pilot study of a 1.5 hour online discussion with N=100 respondents, to capture in-depth responses at scale. Using pre-programmed questions and artificial intelligence (AI) to provide instant visual analyses of responses and additional probes to respondents live, we ran a full qualitative study with a bigger sample in the same duration required for a typical FGD. The discussion was text-based, with respondents being able to view and give their agreement or disagreement to what others may have said without interaction between them. The data was compared to data collected from a previous study with a similar topic and analysed from an operational aspect of conducting research and gathering insights from both methodologies. While this methodology does not replace traditional FGD, it has proven effective in scaling up qualitative research by gathering large amounts of qualitative data within a short duration, in real-time. The methodology has its limitations, primarily the inability to further nuance responses. Despite this, the pilot study appears to be a successful attempt conceptually, as the AI generated valuable instant insights while the study was ongoing, particularly from open-ended (OE) responses. It may add value to specific use cases such as quick sensing which require both breadth and scalability.