These foundational resources introduce the conceptual, theoretical, and methodological foundations of quantitative ethnography, highlighting how QE integrates qualitative, quantitative, and computational methods to investigate patterns of meaning across diverse disciplines and domains.
QE is more than a collection of computational methods; it is an interdisciplinary approach to studying meaning-making that is grounded in theory. QE draws upon traditions in ethnography, learning sciences, learning analytics, philosophy, and computational social science to investigate how we construct meaning through discourse.
The theories presented here provide the conceptual foundations that guide how researchers define research questions, develop coding schemes, interpret patterns, and construct quantitative models. While not every QE study draws on every framework, these perspectives collectively shape the field’s understanding of meaning, context, participation, learning, and social practice.
Meaning-making is the central phenomenon studied in quantitative ethnography. QE seeks to understand how individuals and groups construct, negotiate, and communicate meaning within particular social and cultural contexts.
QE extends the traditions of ethnography by combining rich qualitative interpretation with systematic quantitative representation. It retains ethnography’s emphasis on culture, context, and participant perspectives while incorporating computational methods that support rigorous analysis. That being said, QE methods can be used on other forms of qualitative data and for other purposes than ethnographic analysis.
Epistemic Frame Theory provides one of the primary theoretical foundations for many QE methods, particularly epistemic network analysis (ENA). The theory proposes that participation in professional and disciplinary communities involves developing interconnected networks of knowledge, skills, values, identities, and epistemologies.
QE views discourse as evidence of meaning-making. Drawing from discourse analysis, sociolinguistics, and interaction analysis, QE examines how language, action, interaction, and communication reveal patterns of participation, reasoning, and social practice.
QE conceptualizes research as constructing sophisticated models of meaning-making. Coding schemes, networks, statistical analyses, visualizations, and theories are all models that represent different aspects of a phenomenon. Recent work emphasizes that QE is fundamentally concerned with understanding relationships among models and developing warranted interpretations through iterative refinement.
Theoretical foundations are reflected in several recurring concepts that appear throughout QE:
These concepts connect theory to methodology and provide the foundation for designing, conducting, and interpreting QE research.
These resources introduce the foundational assumptions that distinguish QE from other qualitative, quantitative, and mixed-methods approaches. Topics include:
| Foundational QE Theory | ||
|---|---|---|
| What Good Are Statistics That Don’t Generalize? | Shaffer, D. W., & Serlin, R. C. (2004) | This paper explains how researchers can use statistics to strengthen their understanding of patterns within a particular qualitative study, even when they are not trying to generalize the findings to a larger population. |
| Quantitative Ethnography | Shaffer, D. W. (2017) | Defines QE as a field and establishes its philosophical and methodological foundations. |
| How We Code | Shaffer, D. W., & Ruis, A. R. (2021) | This paper provides guidelines for good coding practice and highlights some of the issues and key questions that quantitative ethnographers and other researchers should consider when coding data. |
| Is QE Just ENA? | Shaffer, D. W., & Ruis, A. R. (2022) | The paper examines whether QE is synonymous with ENA, concluding that although the two are closely connected, neither is exclusive to the other and their relationship is best understood through continued methodological reflexivity. |
| The Foundations and Fundamentals of Quantitative Ethnography | Arastoopour Irgens, G., & Eagan, B. (2023) | Introductory overview of QE's concepts, terminology, and methodological approach. |
| Theories All the Way Across: The Role of Theory in Learning Analytics and the Case for Unified Methods | Shaffer, D. W., & Ruis, A. R. (2024) | This chapter explains that every decision researchers make when turning complex real-world activity into findings is shaped by theory, so combining qualitative and quantitative methods can produce stronger, better-supported conclusions. |
| On Becausality: Revisiting Key Terms of Art in Quantitative Ethnography | Shaffer, D. W. (2026) | Refines the conceptual vocabulary of QE, including models, representation, fairness, warrant, and becausality. |
| Models All the Way Down: Discourse and Other Models in Quantitative Ethnography | Shaffer, D. W., & Ruis, A. R. (2026) | Demonstrates that theory is embedded throughout every stage of QE, from research design and coding to modeling and interpretation. Reinforces that QE is a theory-driven approach. |
| Reviews and State-of-the-Field Articles | ||
|---|---|---|
| Methodology in the Mirror: A Living, Systematic Review of Works in Quantitative Ethnography | Zörgő, S., Peters, G. J. Y., Porter, C., Moraes, M., Donegan, S., & Eagan, B. (2022) | One of the first systematic reviews examining how QE has been conceptualized and implemented across studies. |
| A Systematic Literature Review of Empirical Research on Epistemic Network Analysis in Education | Elmoazen, R., Saqr, M., Tedre, M., & Hirsto, L. (2022) | This review of 76 educational studies finds that ENA is widely useful for visualizing and comparing connections across many forms of data, but most studies rely on small, manually coded samples and do not fully use ENA’s quantitative capabilities |
| Constructing Interpretations with Participants Through Epistemic Network Analysis: Towards Participatory Approaches in Quantitative Ethnography | Vega, H., & Arastoopour Irgens, G. (2022) | This article introduces Participatory QE and shows how researchers can discuss ENA network visualizations with participants so they can help revise codes, identify connections, and co-construct interpretations of their own data. |
| Critical Reviews with Quantitative Ethnography: Theory Use in Literature on Quantified Group Work in Educational Settings | Nøhr, L., Holm-Janas, V., Traxler, A., Bruun, J., Spikol, D., & Misfeldt, M. (2024) | This paper demonstrates how QE can enrich literature reviews by combining network-based mapping with close qualitative interpretation, revealing distinct strands of research on educational group work and differences in how they use learning theory. |
| Finding the Connections: A Scoping Review of Epistemic Network Analysis in Science Education | Reid, J. W., Parrish, J., Syed, S. B., & Couch, B. (2025) | This review of 19 studies shows that ENA can reveal how learners, teachers, and educational materials connect scientific ideas, while highlighting the need for more research in informal settings and at individual and longitudinal levels. |
| Emotional Touchpoints and Emotions of Childbirth: A Systematic Mixed Studies Review and Epistemic Network Analysis | Kuipers, Y. J., Greig, Y., Martinez, A. A., King, M., & Martin, C. H. (2025) | This review finds that women’s childbirth experiences involve closely connected feelings of fear, joy, and sadness shaped by the labor process, their beliefs, and how they cope, highlighting key topics for conversations with maternity care providers. |
| Facilitators and Barriers to Implementing AI in Routine Medical Imaging: Systematic Review and Qualitative Analysis | Wenderott, K., Krups, J., Weigl, M., & Wooldridge, A. R. (2025) | This review of 38 studies finds that successfully introducing AI into routine medical imaging depends on how well it fits clinical workflows and users’ needs, yet existing research focuses far more on technical performance than on clinicians’ experiences and other human factors. |
| Critical Quantitative Ethnography (CritQE): A Pathway Toward Convergence Research and Equity | Garcia, N. M., Onyewuenyi, A. C., & Shaffer, D. W. (2026) | Introduces CritQE as a methodological framework integrating QE with Critical Social Theory and introduces multivocal coding as an alternative to consensus-based approaches |
| Visualizing Racism: Towards a QuantCrit Epistemic Network Analysis | Garcia, N. M., & Eagan, B. (2026) | This article introduces QuantCrit Epistemic Network Analysis (QENA), an anti-racist approach that uses computational visualizations to show how racially marginalized communities understand, navigate, and resist structural racism through discourse. |
| Videos | |
|---|---|
| Going Beyond Mixed Methods to Turn Big Data into Real Understanding | This LAK Keynote given by David Williamson Shaffer introduces quantitative ethnography by exploring learning as enculturation, the integration of qualitative and quantitative methods, the transformation of data into understanding, and epistemic network analysis. |
| QE Visualizations as Tools for Thinking | This talk by Simon Buckingham Shum explores how visualizations communicate research findings, examines ENA and other quantitative ethnography visuals from a human–computer interaction perspective, and considers current and future approaches to presenting analyses and supporting participatory sense-making. |
| Quantitative Ethnography Across Domains: Where We Are and Where We Are Going | In this ICQE Keynote, Golnaz Arastoopour Irgens explores the growth of quantitative ethnography across disciplines, how it combines human interpretation with computational methods to understand complex data, its expanding applications, and opportunities to shape the field’s inclusive and interdisciplinary future. |
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