Data saturation and information power: sample size in qualitative research-when is enough and how to assess?

Qualitative inquiry plays an essential role in optimizing cardiovascular care and facilitating person-centred approaches through understanding in-depth experiences that quantitative data are unable to capture. In quantitative research, rigorous power calculations are used to determine the minimum sample size required to support a finding. In qualitative research, power calculations do not apply, but rather, determination of adequate sample size relies on the concept of data saturation. Operational definitions of data saturation simplify its definition to 'no new information emerging', which can be problematic, particularly if researchers do not provide adequate methodological support to ensure transparency. Information power is a useful concept that provides guidance on estimating sample size prior to the research being conducted (i.e. helpful for grant applications) and during data collection and analysis to determine final sample size. Five considerations to determine the information power of the sample include (i) narrow study aim/objectives, (ii) specific/homogeneous sample, (iii) use of theory, (iv) quality of dialogue, and (v) analysis strategy. This article explores the concept of data saturation, information redundancy, and the alternative concept of information power to determine when there are 'enough' data to support findings. Considerations for each are described. This approach is helpful for researchers conducting qualitative research in determining which approach may be best suited to determine adequate sample size.