Primary Study Quality in Psychological Meta-Analyses: An Empirical Assessment of Recent Practice
Bibliographic record
Abstract
As meta-analytic research has come to occupy a sizeable contingent of published work in the psychological sciences, clarity in the reporting of such work is crucial to its interpretability and reproducibility. This is especially true regarding the assessment of primary study quality, as notions of study quality can vary across research domains. The present study examines the general state of reporting practices related to primary study quality in a sample of 382 published psychological meta-analyses, as well as the reporting decisions and motivations of the authors that published them. Our findings suggest adherence to reporting standards has remained poor for assessments of primary study quality and that the discipline remains inconsistent in its reporting practices generally. We discuss several potential reasons for the poor adherence to reporting standards in our sample, including whether quality assessments are being conducted in the first place, whether standards are well-known within the discipline, and the potential conflation of assessing primary study quality with other facets of conducting a meta-analysis. The implications of suboptimal reporting practices related to primary study quality are discussed.
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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 | 0.840 | 0.945 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.014 |
| Bibliometrics | 0.025 | 0.027 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.008 | 0.012 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".