Data Saturation: The Mysterious Step In Grounded Theory Method
Bibliographic record
Abstract
The aim of this paper is to provide a discussion that is broad in both depth and breadth, about the concept of data saturation in Grounded Theory. It is expected that this knowledge will provide a helpful resource for (a) the novice researcher using a Grounded Theory approach, or for (b) graduate students currently enrolled in a qualitative research course, and for (c) instructors who teach or supervise qualitative research projects. The following topics are discussed in this paper: (1) definition of data saturation in Grounded Theory (GT); (2) factors pertaining to data saturation; (3) factors that hinder data saturation; (4) the relationship between theoretical sampling and data saturation; (5) the relationship between constant comparative and data saturation; and (6) illustrative examples of strategies used during data collection to maximize the components of rigor that Yonge and Stewin (1988) described as Credibility, Transferability or Fittingness, Dependability or Auditability, and Confirmability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.595 | 0.696 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.015 | 0.068 |
| Scholarly communication | 0.017 | 0.027 |
| Open science | 0.012 | 0.029 |
| Research integrity | 0.010 | 0.023 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".