An Equivalence Testing Approach for Evaluating Substantial Mediation
Bibliographic record
Abstract
In the past, researchers often used the nonsignificance of the direct path from the predictor to the outcome, in conjunction with a significant indirect effect, to make claims regarding 'full mediation'. However, the nil hypothesis (i.e., full mediation) is not realistic and it is well known that a nonsignificant test statistic cannot be used to establish the accuracy of a research hypothesis. In this paper, we discuss equivalence testing based procedures for assessing when a mediator explains a substantial proportion of the relationship between a predictor and an outcome. Monte Carlo simulations are used to evaluate the performance of the proposed procedure and compare it against competing alternatives, including traditional tests of full mediation and a proportion mediated approach. The proposed equivalence testing based procedures and the proportion mediated approach performed similarly across the conditions investigated. Recommendations are provided for deciding among the approaches.
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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 | 0.134 | 0.454 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 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".