Code Saturation Versus Meaning Saturation: How Many Interviews Are Enough?

By M. Hennink, B. Kaiser, Vincent C. Marconi. Qualitative Health Research, 2016

3,749 citations recorded by the source corpus.

Saturation is a core guiding principle to determine sample sizes in qualitative research, yet little methodological research exists on parameters that influence saturation. Our study compared two approaches to assessing saturation: code saturation and meaning saturation. We examined sample sizes needed to reach saturation in each approach, what saturation meant, and how to assess saturation. Examining 25 in-depth interviews, we found that code saturation was reached at nine interviews, whereby the range of thematic issues was identified. However, 16 to 24 interviews were needed to reach meaning saturation where we developed a richly textured understanding of issues. Thus, code saturation may indicate when researchers have “heard it all,” but meaning saturation is needed to “understand it all.” We used our results to develop parameters that influence saturation, which may be used to estimate sample sizes for qualitative research proposals or to document in publications the grounds on which saturation was achieved.

Subfields: Education, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/acb1f9ee39499d182b4b2c3795f499568aa4b7b5

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Citations in the corpus

Saturation in qualitative research: exploring its conceptualization and operationalization (2017, neutral): “Hennink et al. (2017) go further, appearing to combine elements of all four models of saturation.”
What Influences Saturation? Estimating Sample Sizes in Focus Group Research (2019, positive): “We then categorized codes for analysis, using the same categorizations as we developed in our earlier work on saturation (Hennink et al., 2017).”
Can sample size in qualitative research be determined a priori? (2018, background): “Hennink, Kaiser, and Marconi (2017) also consider sample size in relation to saturation, distinguishing between what they term ‘code saturation’ (where no additional issues are identified) and ‘meaning saturation’ (where no further insights are gained).”
Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction (2022, neutral): “papers or resources do not yield additional themes [107, 190].”
Comparison of rapid vs in-depth qualitative analytic methods from a process evaluation of academic detailing in the Veterans Health Administration (2019, background): “It is important to keep in mind that the combination of the strength and frequency of qualitative comments is what helps us understand their relative importance and contributions to our research [20], regardless of whether you are using a rapid or in-depth analytic approach.”
Sociotechnical Harms: Scoping a Taxonomy for Harm Reduction (2022, neutral): “Discovery of a new concept or type of harm resulted in a new code, and repeat encounters with existing concepts/harm was documented, in order to reach theoretical saturation – a point at which coding additional papers or resources do not yield additional themes [91, 168].”
A simple method to assess and report thematic saturation in qualitative research (2020, neutral): “[23]) are limited by the total number of interviews conducted: the denominator represents the total number of themes in the fully-analyzed dataset and is fixed, while the number of themes in the numerator gets closer to the denominator with every new interview considered, thus eventually reaching 10”
Saturation controversy in qualitative research: Complexities and underlying assumptions. A literature review (2020, methodology): “Hennink et al. (2017) also suggest that the number of interviews or participants will also differ in in relation to the type of saturation targeted by the research, is it perhaps meaning or code saturation?”
‘Define, Explain, Justify, Apply’ (DEJA): An analytic tool for guiding qualitative research sample size (2021, methodology): “, 2018) or used as a guiding principle to determine the most appropriate sample size (Hennink et al., 2016), as well as being ‘a marker for quality and sample adequacy’ (Mandal, 2018, p.”
Real-Time Type 1 Diabetes Self-Management Decision-Making in Adolescents: Protocol for a Longitudinal Mixed Methods Study Using Text Messaging and Continuous Glucose Monitoring (2026, positive): “A sample size estimate (N=30 ‐ 40) was determined a priori based on estimates of the amount of qualitative data needed to reach data sufficiency across participants with varied glycemic levels and diabetes experiences [43,44].”