A comparison of the type I error rates of three assessment methods for indirect effects
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
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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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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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