Data Saturation

Qualitative research

Definition

The point in qualitative data collection where additional interviews or observations stop producing new codes or themes — the principled answer to 'how many interviews?'

Data saturation is the point in qualitative data collection at which additional interviews or observations stop yielding new codes, categories or insight. It is the standard justification for a qualitative sample size, replacing the statistical calculations used in quantitative work.

Reaching it requires collecting and analysing data concurrently — you cannot recognise saturation after the fact if all interviews were conducted before any coding began. In practice researchers continue for two or three interviews past the apparent point of saturation to confirm that nothing new emerges.

Published guidance suggests homogeneous samples often saturate somewhere between nine and seventeen interviews, though the number depends on the breadth of the question and the diversity of participants. Do not simply assert saturation: document when new codes stopped appearing and how many further interviews confirmed it. Some methodologists prefer information power as a more defensible criterion.