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. [ABSTRACT FROM AUTHOR]