An SEM Perspective on Evaluating Mediation: What Every Clinical Researcher Needs to Know
Bibliographic record
Abstract
After a brief consideration of the definition and importance of mediation, statistical tests for mediation are reviewed, including the joint significance of the two effects involved in the mediation, the Sobel test and its variants, resampling with the bootstrap, Bayesian estimation using MCMC simulation, and the effect ratio. A structural-equation-modeling (SEM) perspective on mediation then introduces the alternative scenarios that could yield a false-positive mediation finding. Design-based, partial solutions are advanced for problems of measurement, uncontrolled common causes, and temporal ordering that can confound mediation analysis. Next, the issue of heterogeneity of effects and statistical interactions in mediation analyses are addressed, including a discussion of moderated mediation and mediated moderation. Finally, the relation of mediation analysis to experimentation is discussed, with attention to the possibility of creatively integrating SEM-based mediation analysis and experimental design.
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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 | 0.198 | 0.322 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.004 | 0.028 |
| Scholarly communication | 0.011 | 0.024 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.007 | 0.019 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".