Two Cross-Platform Programs for Inferences and Interval Estimation About Indirect Effects in Mediational Models
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Bibliographic record
Abstract
In this article, we describe two new programs that compute both p-values and confidence intervals (CI) for the indirect effect in mediational models, including (a) a p-value based on the partial posterior method, which we refer to as p 3 computed across the posterior distribution of the regression coefficients; (b) a variant of p 3 that uses a normal approximation for the posterior distributions, p 3N ; (c) Hierarchical Bayesian CIs (CI HB ) based on the posterior distributions of the regression coefficients; and (d) CIs based on the Monte Carlo method (CI MC ). These programs do not require access to raw data as do resampling methods. Similar to Sobel’s test, p 3 and p 3N constitute a single p-value for the indirect effect while performing substantially better in terms of Type I and II error rates. Furthermore, we include a memory efficient computational algorithm for CI HB and CI MC that allows for precision beyond that in existing alternative implementations. The underlying programs can utilize multicore processors, and their performance is tested through a simulation study. Finally, the use of these programs is illustrated with an empirical example.
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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 | 0.001 | 0.001 |
| 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 | 0.002 |
| 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