Sub‐Sampling at the Researcher's Peril: New Insights Into Sampling Strategy to Avoid Invalid Findings
Bibliographic record
Abstract
ABSTRACT Many researchers currently make scientific claims about a general population that differs in material dimensions from the subsample utilized in the analysis, without fully describing their sample characteristics. It is essential to fully disclose relevant facets of the sample, to enable future stakeholders to make appropriate adjustments: we argue that all publications are valuable independently of the sampling strategy, however; their usefulness will dramatically increase when the authors include all conceivable sample characteristics. By employing a Big‐Data set of over 3,300,000 workers (including 300,000 foreign workers) over 10 years, we illustrate how focusing on narrow subsets of a target group can lead to very different conclusions. We address methodological and ethical challenges for the HRM research field providing recommendations on how to avoid the possibility of flawed validity results and how to make the study more relevant, impactful and ethically robust. For practitioners, we highlight how managers can draw learning from academic studies by appreciating differences in subgroups' outcomes that incorporate “context,” which eventually can inform strategic management and managerial decisions.
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.
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 | 0.627 | 0.761 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".