Digital Straussian Grounded Theory

The Straussian label signals that the study follows the procedural framework Strauss and Corbin codified in 1990 and 1998: systematic open, axial, and selective coding with the paradigm model (conditions, context, actions, consequences) used explicitly during axial coding. This contrasts with Glaserian GT (which rejects prescribed coding families) and constructivist GT (which foregrounds researcher co-construction of categories). The distinction matters for methodology chapters and peer review.

Theoretical saturation is reached when new digital texts, posts, or documents being coded fail to generate new properties or dimensions for existing categories. With voluminous digital corpora this can be deceptive — more data does not mean more categories. The researcher should track each new theoretical sampling round with memos documenting whether new categories emerged; when three or four consecutive rounds produce no new categorical properties, saturation is a defensible claim.

Yes, and they are commonly used. QDA software supports storage, coding, memo-writing, and retrieval — all tasks that are operationally useful in Straussian GT. However, the intellectual work of identifying conceptual categories, constructing the paradigm model, and integrating a core category cannot be automated. Software manages the data; the researcher constructs the theory.

Almost certainly yes. Even when data are technically public, ethical review is standard practice in most institutions for any human-subjects research. Key considerations include whether community members expect their posts to be used for research, whether individuals can be identified, whether the topic is sensitive, and whether platform terms of service permit research use. When in doubt, anonymise all quoted content and seek board review before data collection.

Digital GT does not specify a minimum post count; the criterion is theoretical saturation, not volume. Studies commonly involve dozens to hundreds of posts per sampling round across multiple rounds, but the key driver is whether emerging categories are fully elaborated with varied properties and dimensions — not whether a target number of documents has been reached.