In qualitative research, what is the most important factor in deciding the size of the study sample?

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Data saturation is the key concept guiding qualitative sample size. In this approach, you collect data until no new information, themes, or insights surface with additional interviews or observations. The goal is depth and completeness of understanding for the research question, not to achieve a predefined numeric target. Researchers use purposive or theoretical sampling to seek information-rich cases and then continually analyze data as it’s collected to judge whether new data are adding anything meaningful. When coding and theme development show that subsequent interviews yield few or no novel codes, saturation has likely been reached. In studies with diverse populations, more participants may be needed to achieve saturation across subgroups, while more homogeneous samples may reach saturation quickly. The other concepts are tied to quantitative work—statistical power relates to detecting effects, effect size quantifies magnitude of relations, and sampling bias concerns representativeness; these drive sample size differently and aren’t the primary determinants of qualitative sample adequacy.