Integrating data science with qualitative coding

Thomas M. Menino Convention & Exhibition Center 

With the ubiquity of data science and AI tools, research teams face growing client demands for speedy analyses and quick-turnaround deliverables. However, those tools are not as easily adapted to qualitative coding, which often involves highly nuanced information and unstructured or semi-structured data. Therefore, many qualitative researchers continue to perform the labor-intensive work of manually coding what can amount to thousands of pages of text-based data, straining time and project budgets.

This presentation offers a successful case study for integrating data science tools into qualitative coding to increase efficiency without compromising analytic rigor. We describe the use of large language models to support cleaning and coding qualitative data, and produce structured output that can be seamlessly imported into qualitative analysis platforms such as NVivo for deeper, researcher led coding and interpretation. We also address how to evaluate whether a project is an appropriate candidate for this hybrid approach, discussing when it adds value and efficiency and when it does not.

qualitative research

data science

AI tools

qualitative coding

large language models 

Section on Text Analysis