Abstract:
Open-text questions in quantitative surveys can yield rich information from large samples, but analysing and coding these data using qualitative text analysis is resource-intensive. Large Language Models (LLMs) are a promising tool for scaling up such analyses, reducing time and financial costs. In this paper, we compare the coding accuracy of LLMs with that of student assistants, defining accuracy as agreement with a researcher-coded benchmark dataset. We assess performance on a semi-complex coding task: coding approximately 1,400 open-ended text responses from young US Americans about dating across party-political lines. A researcher-designed coding scheme, developed through thematic qualitative text analysis of the open-text responses, was applied by LLMs and student assistants. We evaluate models from OpenAI, Anthropic, and Mistral, with and without access to training data. The most advanced models outperform student assistants, and performance further increases with training data, highlighting LLMs' capability to code open-text responses. Whereas previous research has mainly focused on social media texts, comparatively simple and surface-level coding tasks, and a technically oriented audience, we contribute to the literature by studying a particularly promising use case of open-ended survey responses and by providing practical recommendations to applied social scientists.
