Digital Constructivist Grounded Theory

The theoretical and analytic logic is identical — iterative sampling, constant comparison, initial and focused coding, memo-writing, and theory generation. The difference lies in the field: Digital CGT operates in internet-mediated spaces, requiring additional methodological attention to platform ethics, data access, the absence of embodied cues, and how digital affordances shape the data.

Technically accessible does not mean ethically appropriate. Even publicly posted data may carry reasonable expectations of privacy depending on community norms, identifiability, and sensitivity of content. Most research ethics frameworks recommend assessing each context separately; sensitive health, mental health, or political data typically require greater justification even when technically public.

CGT does not specify a fixed sample size. Sampling continues — guided by emerging theoretical categories — until theoretical saturation is reached: new data no longer add conceptual variation to the developing theory. In digital contexts this could mean dozens of threads or participants; the criterion is saturation of categories, not volume of data.

In Charmaz's constructivist version, knowledge is seen as co-constructed by the researcher and participants in a specific historical and social context; the researcher's perspective is an acknowledged part of the analysis. Classic Glaserian GT treats categories as 'emerging' from the data as if independently of the researcher. Straussian GT uses a more prescriptive coding and verification procedure. Digital CGT inherits the constructivist reflexivity, which suits the interpretive demands of studying meaning-making in online contexts.

CGT's iterative, line-by-line approach does not scale efficiently to millions of posts. If you have very large datasets, consider whether computational methods (topic modelling, content analysis) better match your goal. Digital CGT is most appropriate for smaller, purposefully sampled corpora where in-depth conceptual development — not frequency counts — is the aim.