A comparison of the type I error rates of three assessment methods for indirect effects
Bibliographic record
Abstract
Mediation analysis is a popular statistical analysis verifying the relation between an independent variable and a dependent variable through a mediator. There are three traditional tests to assess indirect effects: the Baron and Kenny test (BK), the Sobel test (ST) and the bootstrap method (BT). Previous studies have showed that the BT is more powerful and more conceptually appropriate. However, no study has systematically compared these tests regarding the type I error rate. A Monte-Carlo simulation is carried out with 19 scenarios varying paths (but no indirect effect), 9 scenarios varying the direct effect, and 6 sample sizes (1056 different scenarios). Results show that the BT had an overall good performance even for small sample size and whatever the effect sizes. The ST and the BK test were conservative, especially with small sample size and low effect sizes. In conclusion, these tests should be avoided, and the BT is recommended.
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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 | 0.306 | 0.590 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".