General and specific contextual effects in multilevel regression analyses and their paradoxical relationship: A conceptual tutorial
Bibliographic record
Abstract
To be relevant for public health, a context (e.g., neighborhood, school, hospital) should influence or affect the health status of the individuals included in it. The greater the influence of the shared context, the higher the correlation of subject outcomes within that context is likely to be. This intra-context or intra-class correlation is of substantive interest and has been used to quantify the magnitude of the general contextual effect (GCE). Furthermore, ignoring the intra-class correlation in a regression analysis results in spuriously narrow 95% confidence intervals around the estimated regression coefficients of the specific contextual variables entered as covariates and, thereby, overestimates the precision of the estimated specific contextual effects (SCEs). Multilevel regression analysis is an appropriate methodology for investigating both GCEs and SCEs. However, frequently researchers only report SCEs and disregard the study of the GCE, unaware that small GCEs lead to more precise estimates of SCEs so, paradoxically, the less relevant the context is, the easier it is to detect (and publish) small but “statistically significant” SCEs. We describe this paradoxical situation and encourage researchers performing multilevel regression analysis to consider simultaneously both the GCE and SCEs when interpreting contextual influences on individual health.
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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 | 0.078 | 0.157 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.013 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".