Saturation

Conclusions in qualitative research are drawn from patterns a researcher identifies in the data, or conclusions can uncover conceptual (NOT statistical) relationships. As such, we look for points of saturation to know when we have collected and analyzed enough data – and this can determine the sample size and the point at which analysis should end.

Data saturation is the point where new data and theirsorting only confirm the categories (often numbering between three and six orso), themes, and conclusions already reached. Onwuegbuzie and Leech (2007) also discuss theoretical saturation (Strauss & Corbin, 1990), and informational redundancy (Lincoln & Guba, 1985) as specific areas of saturation. There are various strategies for determining when saturation is reached, but researchers should consider a codebook to track themes and findings.

For more information, see the links above, and

Onwuegbuzie, A., & Leech, N. L. (2007). A Call for Qualitative Power Analyses. Quality & Quantity, 41,105–121. DOI 10.1007/s11135-005-1098-1