🤖 AI Summary
This study addresses key limitations in gerontological qualitative research—namely, constraints in data scale, pattern detection, and methodological integration. Methodologically, it pioneers the embedding of machine learning and natural language processing (NLP) techniques directly into qualitative workflows, enabling systematic indexing, multi-scale textual analysis, and reproducible management of participatory observation and in-depth interview data. It integrates open science platforms with qualitative data management systems to support synergistic analysis of large-scale secondary datasets (e.g., the American Voices Project) and original ethnographic fieldwork (e.g., DISCERN dementia ethnography). Contributions include: (1) substantially enhancing qualitative data processing efficiency and analytical transparency; (2) achieving a principled integration of humanistic depth with computational breadth; and (3) advancing aging research toward a multimodal, scalable, and verifiable mixed-methods paradigm.
📝 Abstract
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The depth and context from traditionally qualitative methods such as participant observation, in-depth interviews, and historical documents are increasingly employed alongside scalable data management, computational text analysis, and open-science practices.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.