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Saturation in qualitative research: exploring its conceptualization and operationalization

2017/09/14 by Benjamin Saunders, Julius Sim, Tom Kingstone +5 · 11,914 citations
Decision Sciences · Mathematics · Psychology · Social Sciences · #Artificial intelligence #Computer science #Conceptualization #Confusion #Epistemology #Equivocation #Evaluation and Performance Assessment #Judgement #Mathematics #Operationalization #Psychology #Qualitative Comparative Analysis Research #Qualitative Research Methods and Ethics #Qualitative research #Saturation (graph theory) #Social science #Sociology

paper · pdf · doi:10.1007/s11135-017-0574-8

published in Quality & Quantity 52(4), 1893-1907 (Springer Science+Business Media)

openalex publication_date 2017/09/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06

Abstract

Saturation has attained widespread acceptance as a methodological principle in qualitative research. It is commonly taken to indicate that, on the basis of the data that have been collected or analysed hitherto, further data collection and/or analysis are unnecessary. However, there appears to be uncertainty as to how saturation should be conceptualized, and inconsistencies in its use. In this paper, we look to clarify the nature, purposes and uses of saturation, and in doing so add to theoretical debate on the role of saturation across different methodologies. We identify four distinct approaches to saturation, which differ in terms of the extent to which an inductive or a deductive logic is adopted, and the relative emphasis on data collection, data analysis, and theorizing. We explore the purposes saturation might serve in relation to these different approaches, and the implications for how and when saturation will be sought. In examining these issues, we highlight the uncertain logic underlying saturation-as essentially a predictive statement about the unobserved based on the observed, a judgement that, we argue, results in equivocation, and may in part explain the confusion surrounding its use. We conclude that saturation should be operationalized in a way that is consistent with the research question(s), and the theoretical position and analytic framework adopted, but also that there should be some limit to its scope, so as not to risk saturation losing its coherence and potency if its conceptualization and uses are stretched too widely.

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