An Empirical Review of Research and Reporting Practices in Psychological Meta-Analyses
Bibliographic record
Abstract
As meta-analytic studies have come to occupy a sizable contingent of published work in the psychological sciences, clarity in the research and reporting practices of such work is crucial to the interpretability and reproducibility of research findings. The present study examines the state of research and reporting practices within a random sample of 384 published psychological meta-analyses across several important dimensions (e.g., search methods, exclusion criteria, statistical techniques). In addition, we surveyed the first authors of the meta-analyses in our sample to ask them directly about the research practices employed and reporting decisions made in their studies, including the assessments and procedures they conducted and the guidelines or materials they relied on. Upon cross-validating the first author responses with what was reported in their published meta-analyses, we identified numerous potential gaps in reporting and research practices. In addition to providing a survey of recent reporting practices, our findings suggest that (a) there are several research practices conducted by meta-analysts that are ultimately not reported; (b) some aspects of meta-analysis research appear to be conducted at disappointingly low rates; and (c) the adoption of the reporting standards, including the Meta-Analytic Reporting Standards (MARS), has been slow to nonexistent within psychological meta-analytic 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 | 0.635 | 0.889 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.008 | 0.019 |
| Bibliometrics | 0.026 | 0.043 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".