Grounded theory, mixed methods, and action research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
These commonly used methods are appropriate for particular research questions and contexts Qualitative research includes a variety of methodological approaches with different disciplinary origins and tools. This article discusses three commonly used approaches: grounded theory, mixed methods, and action research. It provides background for those who will encounter these methodologies in their reading rather than instructions for carrying out such research. We describe the appropriate uses, key characteristics, and features of rigour of each approach. Grounded theory was developed by Glaser and Strauss.[1] Its main thrust is to generate theories regarding social phenomena: that is, to develop higher level understanding that is “grounded” in, or derived from, a systematic analysis of data. Grounded theory is appropriate when the study of social interactions or experiences aims to explain a process, not to test or verify an existing theory. Researchers approach the question with disciplinary interests, background assumptions (sometimes called “sensitising concepts”[2]) and an acquaintance with the literature in the domain, but they neither develop nor test hypotheses. Rather, the theory emerges through a close and careful analysis of the data. Key features of grounded theory are its iterative study design, theoretical (purposive) sampling, and system of analysis.[3] An iterative study design entails cycles of simultaneous data collection and analysis, where analysis informs the next cycle of data collection. In a study of the experience of caring for a dying family member, for instance, preliminary analysis of interviews with family care providers may suggesta theme of “care burdens,” and this theme could be refined by interviewing participants who are at variouspoints in the care trajectory, who might offer different perspectives. Analysis of the subsequent phase of data collection will lead to further adaptations of the data collection process to refine and complicate the emerging theory of care burdens. In keeping with this …
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it