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The depth and context from traditionally qualitative methods such as participant observation, in-depth interviews, and historical documents are increasingly employed alongside scalable data management, computational text analysis, and open-science practices.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 2 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.
Deep Dive into Arxiv 2512.17850.
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The depth and context from traditionally qualitative methods such as participant observation, in-depth interviews, and historical documents are increasingly employed alongside scalable data management, computational text analysis, and open-science practices.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 2 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 ch
Qualitative research methods--especially participant observation and in-depth interviewing--have long been central to social scientific understandings of aging. By immersing themselves in the communities, institutions, and cultural contexts where older adults live, qualitative researchers highlight processes and experiences that broader survey methods might miss or only partially capture (Gubrium, 1997;Hochschild, 1973;Kaufman, 1986;Loe, 2011;Myerhoff, 1980;Newman, 2004). From Myerhoff's (1980) depiction of Jewish immigrant identities, to Hochschild's (1973) observation of how social engagement can be maintained among older adults, and Gubrium's (1976Gubrium's ( /1997) analysis of institutional life, participant observation and indepth interviews reveal everyday life worlds, interactions, and repertoires that shape possibilities in everyday life. This provides a necessary vantage to understand how older adults navigate complex and often unequal social structures with granularity a single method cannot capture, as well as how institutions shape the lives of older adults (Abramson, 2015;Ferraro, 2018;Torres, 2025).
Although qualitative methods offer the possibility of rich, contextual understandings central to sociological inquiry (Cicourel, 1982), they have historically faced challenges around scope, transparency, replicability, and scalability (Abramson & Dohan, 2015;Goldthorpe, 2000;Murphy et al., 2021). Yet, the landscape of qualitative research is changing. In studies of aging, ethnographic approaches are increasingly a steady fixture, in journal articles and competitive grants as well as scholarly monographs (Abramson, 2021;Phoenix, 2018). Meanwhile, ongoing transformations in digital technology–including artificial intelligence (AI) and computational social science–present new avenues for analyzing data at scale, including information in the form of qualitative text like transcripts and fieldnotes, alongside new challenges (Burrell & Fourcade, 2021;Farber et al., 2025;Li & Abramson, 2025;Nelson, 2020;Roberts et al., 2022). Researchers increasingly ask whether the interpretive depth of qualitative methods can be maintained–or possibly enriched–by contemporary computational tools, machine-assisted workflows, and larger qualitative data sets (Abramson et al., 2018(Abramson et al., , 2026;;Brower et al., 2019;Chandrasekar et al., 2024;Small, 2011). These questions echo broader shifts in sociology, where computational social science (CSS) analyses of “big data” intersect with traditional methodological approaches (Breiger, 2015;Nelson et al., 2021).
An increasing body of literature identifies both pathways and alternatives to traditional approaches in an era of increasing computation (Abramson et al., 2026;Bail, 2024;Edelmann et al., 2020). In a recent Annual Review of Sociology piece, following the twenty-fifth anniversary of the classical debates on the use of computers in systematic qualitative analysis, my colleagues and I argued that computational–qualitative engagements often mirror a typology of practices that either extend or reconfigure qualitative boundaries by working to–(1) streamline and augment existing workflows, (2) scale up samples and projects, (3) generate hybrid analytical approaches, and (4) interrogate the sociology of computation itself, both as a topic and act of self-reflection (Abramson et al., 2026). These uses connect directly to growing works examining questions of aging–both in our own studies and in national initiatives. For example, the American Voices Project (AVP) shares large-scale in-depth interview data with teams working on projects using “big qualitative data” to reveal health inequalities across the life course in ways not possible in smaller-scale qualitative work (Abramson et al., 2024). Meanwhile, the National Dementia Workforce Study (NDWS) offers a new example that links survey data to computationally enhanced qualitative approaches to understand the care economy and workforce challenges in dementia care within and across occupational groups (Maust et al., 2025). Team-based ethnographic studies likewise use computational analyses to validate patterns and typologies (Arteaga et al., 2025). New data sets for science and policy revisit the possibility of public use portals with deidentified qualitative data for expanding the scale and accessibility of qualitative text (Abramson & Dohan, 2015;Edin et al., 2024;Gupta et al., 2021;Mauldin et al., 2024). Taken together, such cases show how the intertwined development of computational and qualitative projects can deepen our capacity to understand complex phenomena such as human aging.
Participant observation and in-depth interviews conducted in classic ways remain indispensable for understanding older adults’ lives, experiences, and trajectories. Computationally assisted approaches conducted at scale, involve different study designs, and are not a direct analog or replacement (Abramson & Dohan, 2015;Grigoropoul
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