Developing Sampling Frame for Case Study: Challenges and Conditions
Bibliographic record
Abstract
Due to statistical analysis, the issue of random sampling is pertinent to any quantitative study. Unlike quantitativestudy, the elimination of inferential statistical analysis, allows qualitative researchers to be more creative in dealingwith sampling issue. Since results from qualitative study cannot be generalized to the bigger population, qualitativeresearchers do not have to endure the strenuous randomization process of sampling procedure. However, qualitativeresearchers should not take sampling procedures too lightly, and if they do, it will affect the richness and theappropriateness of the data. The chances are, the data will not answer their research questions and this can frustratethe researchers when making meanings to the data. This paper will examine the available methods in samplingparticipants for qualitative study. Specifically, the paper will discuss the sampling frame suitable for case study, suchas single-case (holistic and embedded), multi-case, and a snowball or network sampling procedure. Discussion willalso involve challenges anticipated for each procedure.
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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 | 0.147 | 0.145 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".