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. 2017 Mar;27(4):591-608.
doi: 10.1177/1049732316665344. Epub 2016 Sep 26.

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

Monique M Hennink  1 , Bonnie N Kaiser  2 , Vincent C Marconi  1   3
Affiliations

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

Monique M Hennink et al. Qual Health Res. 2017 Mar.

Abstract

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.

Keywords: HIV/AIDS; USA; behavior; in-depth interviews; infection; methodology; qualitative; saturation.

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Conflict of interest statement

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Figure 1.
Figure 1.
Overview of analytic methods for assessing code saturation and meaning saturation.
Figure 2.
Figure 2.
Timing of code development. Note. Interviews 1 to 16 were with out-of-care patients, and Interviews 17 to 25 were with in-care patients.
Figure 3.
Figure 3.
Timing of code development for randomized versus actual order of interviews.
Figure 4.
Figure 4.
Code prevalence and timing of code development, by type of code. Note. The horizontal axis shows when codes were developed during the process of codebook development. All interviews were then coded using the final codebook, and the number of interviews where a code was applied is shown on the vertical axis. IDI = In-depth interview.
Figure 5.
Figure 5.
Timing of code development versus timing of meaning saturation. Note. Code saturation is depicted at Interview 9 which reflects our finding from earlier analyses and refers to code saturation across the entire data set.
Figure 6.
Figure 6.
Parameters of saturation and sample sizes.
See this image and copyright information in PMC

References

    1. Attia S, Egger M, Müller M, Zwahlen M, & Low N (2009). Sexual transmission of HIV according to viral load and antiretroviral therapy: Systematic review and meta-analysis. AIDS, 23, 1397–1404. - PubMed
    1. Bowen G (2008). Naturalistic inquiry and the saturation concept: A research note. Qualitative Research, 8, 137–152.
    1. Bryant A, & Charmaz K (Eds.). (2007). The SAGE handbook of grounded theory. London: Sage.
    1. Bryman A (2012). Social research methods (4th ed.). Oxford, UK: Oxford University Press.
    1. Carlsen B, & Glenton C (2011). What about N? A methodological study of sample-size reporting in focus group studies. BMC Medical Research Methodology, 11, Article 26. - PMC - PubMed