Qualitative data saturation in health sciences research
Why you should read this article:
To differentiate theoretical saturation and data saturation
To explore the possible stages that may signal data saturation
To identify ways to achieve qualitative data saturation and the pitfalls
To examine suggested sample sizes in qualitative data saturation
Background Deciding when and where to stop gathering data is a significant challenge for novice and even seasoned qualitative researchers. Qualitative data saturation (QDS) is a well-known concept, but some researchers may struggle to identify explicit indications and stages of saturation.
Aim To use the literature and the author’s experiences to discuss possible benchmarks that researchers may find helpful when collecting qualitative data.
Discussion This article considers how to operationalise data saturation, data saturation points, and quality and quantity of data in saturation, as well as some possible pitfalls.
Conclusion The concept of saturation is most effectively contextualised within a study design when inductive reasoning is employed. Deductive reasoning may prove beneficial to qualitative researchers when predetermined averages of previous study samples in a similar context are used as a guide.
Implications for practice The author proposes effective approaches to QDS as a guide for future qualitative research.
