Data Saturation in Qualitative Research
Data saturation is a foundational principle in qualitative research describing the point at which data collection yields no new themes, codes, or insights—additional data becomes redundant. Introduced by Glaser and Strauss (1967) in their work on grounded theory, saturation guides decisions about sample size and when to stop recruiting participants. Saturation is not a fixed number but a dynamic endpoint determined by examining whether new data are adding substantively new information. The concept is central to claims of rigor and theoretical adequacy in qualitative research, signaling that the researcher has gathered sufficient data to understand the phenomenon in depth.
Record di origine
Citazioni copiate testualmente dal record di origine del metodo. Non si inferisce alcuna verifica a livello di affermazione da esse.
- Glaser, B. G., & Strauss, A. L. (1967). The Discovery of Grounded Theory: Strategies for Qualitative Research. Aldine. · ISBN 978-0202302560
- Strauss, A., & Corbin, J. (1998). Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory (2nd ed.). SAGE Publications. · ISBN 978-0803959393
- Bowen, G. A. (2008). Naturalistic inquiry and saturation (S): Determining when enough is enough. Journal of Research in Education, 18(1), 137-152. · URL
- Guest, G., Bunce, A., & Johnson, L. (2006). How many interviews are enough? An experiment with data saturation and variability. Field Methods, 18(1), 59-82. · DOI 10.1177/1525822X05279903
Affermazioni curate
Affermazioni persistite nel registro delle evidenze, ciascuna con la propria valutazione.
Questa vista non inventa una valutazione dell'affermazione quando il registro non ne ha.
Metodi correlati
Generato dal grafo dei metodi e mostrato come relazioni suggerite dalla macchina — nessuna affermazione di evidenza viene inferita.