Comparisons of Adaptations in Grounded Theory and Phenomenology: Selecting the Specific Qualitative Research Methodology
Bibliographic record
Abstract
The most widely used qualitative research methodologies are grounded theory and phenomenology. Both methodologies have expanded over time to several adaptations aligning with different paradigms, complex philosophical assumptions, and varying methodological strategies. Novice researchers either mistakenly mix the strategies of both methodologies or blend specific assumptions of methodologies’ different adaptations. Choosing the appropriate methodology and the specific adaptation in line with research inquiry and congruent with the researchers’ worldview is crucial in undertaking rigorous qualitative study. To date, there is limited literature that compared and contrasted the varying philosophical underpinnings of the two methodologies’ different adaptations. The purpose of this methodological paper is to provide a general overview of the two methodologies’ different adaptations to illustrate how they differ in approach. By immersing into the origins, philosophical assumptions, and utility of the two methodologies’ adaptations, novice researchers will develop a general overview of the foundations that support those specific adaptations. Finally, the considerations in choosing a specific adaptation of a methodology are discussed and applied by underpinning a research question on the care experiences of patients in the Accountable Care Unit. Thus, this methodological paper may assist novice researchers in deciding which specific adaptation of the two methodologies is the appropriate qualitative methodology for their research.
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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 | 0.424 | 0.528 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".