Conducting Qualitative Interviews with AI

Abstract

Qualitative interviews are one of the fundamental tools of empirical social science research and give individuals the opportunity to explain how they understand and interpret the world, allowing researchers to capture detailed and nuanced insights into complex phenomena. However, qualitative interviews are seldom used in economics and other disciplines inclined toward quantitative data analysis, likely due to concerns about limited scalability, high costs, and low generalizability. In this paper, we introduce an AI-assisted method to conduct semi-structured interviews. This approach retains the depth of traditional qualitative research while enabling large-scale, cost-effective data collection suitable for quantitative analysis. We demonstrate the feasibility of this approach through a large-scale data collection to understand the stock market participation puzzle. Our 395 interviews allow for quantitative analysis that we demonstrate yields richer and more robust conclusions compared to qualitative interviews with traditional sample sizes as well as to survey responses to a single open-ended question. We also demonstrate high interviewee satisfaction with the AI-assisted interviews. In fact, a majority of respondents indicate a strict preference for AI-assisted interviews over human-led interviews. Our novel AI-assisted approach bridges the divide between qualitative and quantitative data analysis and substantially lowers the barriers and costs of conducting qualitative interviews at scale.

Keywords: Artificial Intelligence, Interviews, Large Language Models, Qualitative Methods, Stock Market Participation

JEL Classification: C83, C90, D14, D91, Z13