This Research Topic addresses the opportunities and challenges of integrating quantitative methods in the study of social interaction. Over the past few decades, the quantification and statistical analysis of linguistic data have gained significant traction across various subfields of linguistics. More recently, scholars within Conversation Analysis (CA) and related disciplines such as Interactional Linguistics have begun to question the field’s traditionally exclusive reliance on qualitative methods, particularly sequential analysis. They argue that in order to strengthen the empirical basis of findings and increase the visibility and impact of interactional research, it is essential to incorporate larger datasets and state-of-the-art quantitative methods.

This shift, however, has not gone uncontested. Some CA practitioners have raised concerns that quantitative approaches risk flattening the complexity of interactional data. Since the circumstances of any given interaction are always locally occasioned and contingent, coding practices—however carefully designed—inevitably require abstraction. As Schegloff (1993, 103) famously noted, interactions are not “analytically coherent universes.” Consequently, coding can be seen as forcing the multifaceted realities of talk-in-interaction into predefined categories, potentially obscuring nuances that are meaningful to participants themselves.

We fully acknowledge these concerns and emphasize that quantitative analysis cannot—and should not—replace the detailed, context-sensitive work that lies at the heart of CA. However, we maintain that many core questions in the field are best addressed through a combination of methods. Mixed-methods approaches, which integrate the precision and generalizability of quantitative analysis with the depth and sensitivity of qualitative interpretation, offer powerful tools for advancing our understanding of intersubjective meaning-making. Crucially, such approaches require datasets that are not only large enough to support statistical claims but also representative and sufficiently rich to allow for microanalytic scrutiny.

With this Research Topic, we aim to bring together contributions that explore how qualitative and quantitative methods can be fruitfully combined in the analysis of social interaction. We welcome empirical studies that demonstrate innovative mixed-methods approaches, critical reflections on the methodological and epistemological implications of quantification in CA, as well as theoretical contributions that address the potential tensions between analytic depth and generalizability. In addition, we are further interested in work that engages with the practical challenges of implementing such approaches, such as increased workload, the development of reliable coding schemes, and the need for interdisciplinary collaboration and computational tools. Our goal is to foster dialogue across methodological boundaries and to advance a more robust, scalable, and reflexive form of interaction research; one that remains grounded in the core principles of CA and keeps an open mind to the opportunities and challenges of mixing methods.

Relevant subtopics include, but are not limited to:

• challenges and strategies for coding interactional data
• quantitative and corpus-based approaches to visual and embodied meaning-making
• the integration of multimodal cues (e.g., gaze, gesture, and posture) into pattern analysis
• the role of sequence analysis in identifying and validating recurrent interactional patterns
• mixed-methods approaches in the analysis of preference structures, repair, turn-taking, or action formation
• cross-linguistic or cross-cultural comparisons of interactional practices using large datasets.

We particularly encourage submissions that reflect on the epistemological implications, practical hurdles, and analytical affordances of quantification in the context of richly situated interactional practices. Studies that draw on technological innovations (e.g., mobile eyetracking, AOI technology, pupillometry, and motion capturing) to record and analyze interactional data are particularly welcome.

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Beyond Sequence Analysis: Integrating Quantitative Methods into Conversation Analysis