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      Saturation in qualitative research: exploring its conceptualization and operationalization.

      Quality & Quantity
      Springer Nature
      Grounded theory, Data collection, Qualitative research, Data analysis, Saturation

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          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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          Most cited references46

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          Are We There Yet? Data Saturation in Qualitative Research

          Failure to reach data saturation has an impact on the quality of the research conducted and hampers content validity. The aim of a study should include what determines when data saturation is achieved, for a small study will reach saturation more rapidly than a larger study. Data saturation is reached when there is enough information to replicate the study when the ability to obtain additional new information has been attained, and when further coding is no longer feasible. The following article critiques two qualitative studies for data saturation: Wolcott (2004) and Landau and Drori (2008). Failure to reach data saturation has a negative impact on the validity on one’s research. The intended audience is novice student researchers.
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            Choose your method: a comparison of phenomenology, discourse analysis, and grounded theory.

            The purpose of this article is to compare three qualitative approaches that can be used in health research: phenomenology, discourse analysis, and grounded theory. The authors include a model that summarizes similarities and differences among the approaches, with attention to their historical development, goals, methods, audience, and products. They then illustrate how these approaches differ by applying them to the same data set. The goal in phenomenology is to study how people make meaning of their lived experience; discourse analysis examines how language is used to accomplish personal, social, and political projects; and grounded theory develops explanatory theories of basic social processes studied in context. The authors argue that by familiarizing themselves with the origins and details of these approaches, researchers can make better matches between their research question(s) and the goals and products of the study.
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              Sample size for qualitative research

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                Author and article information

                Journal
                29937585
                5993836
                10.1007/s11135-017-0574-8

                Grounded theory,Data collection,Qualitative research,Data analysis,Saturation

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