To saturate or not to saturate? Questioning data saturation as a useful concept for thematic analysis and sample-size rationales

The concept of data saturation, defined as 'information redundancy' or the point at which no new themes or codes 'emerge' from data, is widely referenced in thematic analysis (TA) research in sport and exercise, and beyond. Several researchers have sought to 'operationalise' data saturation and provide concrete guidance on how many interviews, or focus groups, are enough to achieve some degree of data saturation in TA research. Our disagreement with such attempts to 'capture' data saturation for TA led us to this commentary. Here, we contribute to critical discussions of the saturation concept in qualitative research by interrogating the assumptions around the practice and procedures of TA that inform these data saturation 'experiments', and the conceptualisation of saturation as information redundancy. We argue that although the concepts of data-, thematic- or code-saturation, and even meaning-saturation, are coherent with the neo-positivist, discovery-oriented, meaning excavation project of coding reliability types of TA, they are not consistent with the values and assumptions of reflexive TA. We encourage sport and exercise and other researchers using reflexive TA to dwell with uncertainty and recognise that meaning is generated through interpretation of, not excavated from, data, and therefore judgements about 'how many' data items, and when to stop data collection, are inescapably situated and subjective, and cannot be determined (wholly) in advance of analysis.

Automated summary

The concept of data saturation, often used in thematic analysis research, is questioned in this commentary which highlights the differences between neo-positivist and reflexive approaches. It emphasizes the subjective nature of determining data saturation and advocates for researchers to embrace uncertainty and interpretation in qualitative research.