Patrick Dunleavy and Timothy Montieth Qualitative social scientists have lagged behind their quantitative colleagues in adopting open social science approaches to research. In their new open access book, “Doing Open Social Science” from LSE Press, Patrick and Timothy outline numerous different strategies that can allow qualitative researchers to better demonstrate the evidence for their work to readers and re-users of their work. For researchers using qualitative methods open social science can seem to pose far greater difficulties than those faced by quantitative researchers. While replicating a quantitative analysis on the same dataset is relatively straightforward, interviews, focus groups, case studies and fieldwork pose problems for reproducibility because they cannot be repeated. They are necessarily events that are unique in space, time and participants. And yet, text, audio and visual records may allow for re-analysis. Similarly, boosting pre-registration may appear to require that researchers know in advance and in detail what they will find in the field – whereas interviews, case studies and fieldwork are essentially exploratory approaches. In all qualitative work project teams may set out initial expectations and intentions in pre-registered documents, but they must always remain open and alert to discoveries, new meanings and unexpected developments that shift their research trajectories. Nothing is more fatal for the reliability, integrity and accuracy of qualitative work than researchers who are so constrained by their prior understandings of what happened, what things mean, or how social processes work, that they approach research participants and experiences with closed minds. Open social science methods can be applied across all the major qualitative methods In systematic documentation work the existence of records that can be reinspected mitigates reproducibility issues. Pre-registering the scope and intent of the planned research is somewhat easier. But again, the pathways that research takes must adapt to the gains in knowledge that researchers make from immersive reading and intensively analysing texts. In archival work recognition of this imperative has been strengthened amongst historians and social scientists by an acute awareness of the limits of what is recorded and how, which always necessitates critical interpretation of texts. Finally, some pre-registration and disclosure components of open social science approaches may seem to run at a tangent (or even counter) to the growing shift towards co-production and citizen social science. In our book Doing Open Social Science: A Guide for Researchers we dedicate a whole section to demonstrate how these difficulties are in fact tractable. Open social science methods can be applied across all the major qualitative methods. Indeed, as open social science practices spread and are applied widely and innovatively in qualitative work, so the range of feasible solutions to problems will also expand. The effort needed to translate open practices into qualitative social science can also be productive, stimulating new ideas and creating social science that is trusted by researchers and the public. Practical steps towards greater openness We cover here the most frequently used qualitative methods approaches. Interviews Only on-the-record interviews can be fully reproducible, while those that are non-attributable or off the record pose substantial problems for researchers seeking to demonstrate that their work is well-founded (Chapter 9). Yet many practical steps can be taken to pre-register the main elements of interview work and to increase assurance about the content of more closed forms of interview. For instance: - Encouraging interviewees to adopt more open formats, such as blogging on the record, or to shift from off-the-record to non-attributable, or on-the-record interviews (e.g. on Zoom or Teams); - making better and fuller recordings of what gets said, especially in focus groups and multi-way interviews; - opening up more information about non-attributable interviews in ‘jittered’ ways that still protect confidentiality, and - creating quantitative metrics from interview data using innovative methods. Case studies Different sets of issues arise around single or very small numbers of in-depth or ‘diagnostic’ case studies, medium numbers of intermediate cases and larger numbers of short ‘apt illustrations’. We show how readers and research re-users can be given strengthened confidence in the work undertaken in each of these situations, especially where cases are anonymized, as they often must be. Key steps involve: - Being clear about the casing strategy being followed; - giving enhanced contextual settings for cases, and - explaining how ‘saturation’ or ‘enough’ numbers for interviews were determined. And newer methods focus on: - qualitative comparative analysis (QCA), to explore the logic of multi-causal routes to the same outcomes - using cases within mixed methods studies, and -developing comprehensive evidence-synthesising approaches (such as ‘systematic review’ of cases in a given sub-field). All these approaches can generate valuable extra insights and assurance. Similar steps can also increase the usefulness of fieldwork, as well – for instance, strengthening the immediacy, granularity and later visibility of observations using computerised field journals and notebooks. Systematic documentation analysis If the base records being analysed are easily available in full for (digital) reinspection by later project teams, then the potentials for extended forms of reproducibility and re-audit are greater. Researchers can also do a good deal in ‘show and tell’ mode to help readers and re-users understand the character and limits of the original texts being analysed, and to demonstrate that their own interpretation is accurate (Chapter 11). However, where re-accessing the base texts is not possible or practicable, as it often is not, then some similar issues with other qualitative work discussed above may recur. They can again be addressed by: - adapted forms of pre-registration of the scope and size of work undertaken; - explaining exploratory adaptations to the planned scope or direction of research; – curating a selection of key or illustrative documents to help later re-users, wherever feasible; - counting within and across texts (even if the researchers’ own main approach is immersive reading); - coding text to make checks on the evidence for qualitative interpretations; and - 9developing quantitative metrics to back up qualitative interpretations – for instance, via website analyses across multiple agencies or organisations. Progress in developing the openness of historic archival research has been more limited, with no or scant practicable reproducibility for much older archival evidence However, calls for improving on previously standard but limited historical scholarship methods (footnotes to an archive that readers cannot themselves access) have been made. And ‘digital history’ approaches have greatly diversified options. Although the potential for full digitisation of older historical sources remains vast, social scientists and historians can help develop project-level openness for the materials they use, and help overcome legacy cultural and library constraints in moving archival work into more open practices. Citizen social science and co-production These approaches to social science have somewhat divergent aims from open social science for example, by prioritising the privacy/security interests of research participants themselves over demonstrating reproducibility. And some forms of citizen social science (notably deliberative democracy) have paid little heed to openness considerations, although the most participatory approaches have done so. At root, however, both the citizen and open social science and the streams of innovation are similarly aligned in stressing: - the ultra-careful initial development of research proposals, and - ensuring that research participants’ meanings and experiences are assigned enhanced importance, are sympathetically documented, and are accurately interpreted with participants’ own involvement. Looking forward Over the next ten to twenty years we will likely see a rapid evolution of qualitative research techniques under the impetus of AI and data science improvements, with large language models (LLMs) already revolutionising much of the analysis and production of qualitative research. New research agents will shortly be available to strengthen and better assure the evidence base for qualitative work as academic standards rise in the third digital era wave. The pathways to achieving a higher status and replicability for qualitative social science are already mapped and we are optimistic of the potential for further rapid improvements. This blogpost draws on Part 3 of Patrick Dunleavy and Timothy Monteath’s book, Doing Open Social Science: A Guide for Researchers , published open access by LSE Press on 14 May 2026. It draws on CIVICA I research funding from the EU’s Horizon Programme. About the authors Patrick Dunleavy is Emeritus Professor of Political Science and Public Policy at the London School of Economics and Political Science, and a Fellow of the British Academy and of the Academy of Social Sciences. Timothy Monteath is Assistant Professor in Data Visualization in the Centre for Interdisciplinary Methods at the University of Warwick, and previously was Researcher on the LSE’s Civica open social science project, where he also completed his doctorate in Sociology. This text first appeared on LSE Impact blog 13 May 2026