A comparison of meta‐methods for synthesizing indirect effects
Bibliographic record
Abstract
Synthesizing findings about the indirect (mediated) effect plays an important role in determining the mechanism through which variables affect one another. This simulation study compared six methods for synthesizing indirect effects: correlation-based MASEM, parameter-based MASEM, marginal likelihood synthesis, an adjustment to marginal likelihood synthesis, and univariate, and two-parameter sequential Bayesian methods. This paper provides an empirical example and code for using all methods compared in the simulation study. The methods were compared on (relative) bias, precision, and RMSE of the point estimates and the power, coverage, and type I error rates of the interval estimates. The factors in the simulation were the methods, the strength of the indirect effect, the measurement level of the independent variable, and the number of studies available for synthesis. Correlation-based MASEM had the lowest bias out of all methods and produced interval estimates with the best statistical properties. The precision of the point estimates and the RMSE was marginally different across methods. Marginal likelihood synthesis had the highest power but performed poorly in terms of coverage and type I error rates. The adjusted marginal likelihood synthesis and two-parameter sequential Bayesian methods performed adequately in terms of bias and power, and the adjusted marginal likelihood synthesis had higher power than the sequential Bayesian method. Correlation-based MASEM performed best out of the six methods. Guidelines for optimal practices when synthesizing indirect effects (eg, required number of studies, type of results reported) are provided, as well as suggestions for further methodological research.
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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 | 0.312 | 0.595 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.024 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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; 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".