checking-theory-saturation

Theoretical saturation requires evaluating new concept rates, category completeness, relationship stability, and theory completeness. This Skill compares existing coded artifacts against new data batches to compute measurable saturation rates and decide whether to stop or continue sampling.

Qualitative coding can stop when new concept emergence halts, categories have adequate properties and examples, relationships stop yielding genuinely new links, and core theoretical components like propositions and boundaries are sufficiently developed. This Skill measures all four criteria quantitatively.

Yes, it applies to qualitative coding workflows where researchers compare existing codes, categories, and relations against newly analyzed data. You provide your existing theory artifacts and the latest batch of coded data for the assistant to evaluate saturation status.

The Skill depends on pandas, numpy, and scikit-learn to compute saturation metrics. These libraries process coded data to calculate new concept rates, completeness rates, and relationship stability for the saturation assessment.

Category completeness verifies whether each category has adequate definition, properties or attributes, and supporting examples or cases. This Skill assesses completeness rates by checking if newly analyzed data continues to fill gaps in your existing category definitions.

Automated saturation checks may not suit early-stage grounded theory research where core categories are still forming. If your theory lacks defined propositions, a core category, or established boundaries, the completeness and stability metrics will not yield meaningful stop or continue signals.