Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| NVivo et ATLAS.ti pour l'analyse qualitative× | Saturation des données en recherche qualitative× | |
|---|---|---|
| Domaine | Recherche qualitative | Recherche qualitative |
| Famille | Process / pipeline | Process / pipeline |
| Année d'origine≠ | 1999 | 1967 |
| Auteur d'origine≠ | QSR International (NVivo) and Scientific Software-Citational (ATLAS.ti) | Barney Glaser and Anselm Strauss |
| Type≠ | Tool | Concept |
| Source fondatrice≠ | Lewins, A., & Silver, C. (2007). Using Software in Qualitative Research: A Step-by-Step Guide. SAGE Publications. ISBN: 978-1412903653 | Glaser, B. G., & Strauss, A. L. (1967). The Discovery of Grounded Theory: Strategies for Qualitative Research. Aldine. ISBN: 978-0202302560 |
| Alias≠ | CAQDAS, QDA software, qualitative analysis software, NVivo | saturation, theoretical saturation, thematic saturation, sampling to saturation |
| Apparentées | 4 | 4 |
| Résumé≠ | NVivo and ATLAS.ti are Computer-Assisted Qualitative Data Analysis Software (CAQDAS) programs that facilitate coding, organizing, and analyzing qualitative data—including text (transcripts, documents), images, video, and audio. NVivo, developed by QSR International, is widely used in academic research and supports data organization, coding, memo-writing, retrieval, and analysis visualizations. ATLAS.ti, developed by Scientific Software-Citational, emphasizes hermeneutic interpretation and network visualization. Both tools were introduced in the late 1990s and have become standard across disciplines. CAQDAS is not analysis itself—the researcher must make analytical decisions—but rather augments human analysis by managing large data volumes, organizing codes systematically, tracking analysis decisions, and generating visualizations. These tools improve transparency and rigor 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. |
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