Integrating Computational Methods and AI into Qualitative Studies of Aging and Later Life
It addresses the problem of integrating computational methods into qualitative studies of aging for researchers, but is incremental as it builds on existing tools without introducing new paradigms.
This chapter demonstrates how computational social science tools, including machine learning and natural language processing, are used to enhance qualitative aging research by aggregating and analyzing large volumes of data, such as from ethnographies and interviews, to identify patterns and scale up studies. It highlights potential benefits like streamlining workflows and generating multi-method approaches, while noting challenges and the need to broaden rather than replace traditional methods.
This chapter demonstrates how computational social science (CSS) tools are extending and expanding research on aging. The depth and context from traditionally qualitative methods such as participant observation, in-depth interviews, and historical documents are increasingly employed alongside scalable data management, computational text analysis, and open-science practices. Machine learning (ML) and natural language processing (NLP), provide resources to aggregate and systematically index large volumes of qualitative data, identify patterns, and maintain clear links to in-depth accounts. Drawing on case studies of projects that examine later life--including examples with original data from the DISCERN study (a team-based ethnography of life with dementia) and secondary analyses of the American Voices Project (nationally representative interview)--the chapter highlights both uses and challenges of bringing CSS tools into more meaningful dialogue with qualitative aging research. The chapter argues such work has potential for (1) streamlining and augmenting existing workflows, (2) scaling up samples and projects, and (3) generating multi-method approaches to address important questions in new ways, before turning to practices useful for individuals and teams seeking to understand current possibilities or refine their workflow processes. The chapter concludes that current developments are not without peril, but offer potential for new insights into aging and the life course by broadening--rather than replacing--the methodological foundations of qualitative research.