An SEM Perspective on Evaluating Mediation: What Every Clinical Researcher Needs to Know
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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