Measurement Practices in Large-Scale Replications: Insights from Many Labs 2
Bibliographic record
Abstract
Validity of measurement is integral to the interpretability of research endeavours and any subsequent replication attempts. To assess current measurement practices and the construct validity of measures in large-scale replication studies, we conducted a systematic review of measures used in Many Labs 2: Investigating Variation in Replicability Across Samples and Settings (Klein et al., 2018). To evaluate the psychometric properties of the scales used in ManyLabs 2 we conducted factor and reliability analyses on the publicly-available data. We report that measures in Many Labs 2 were often short with little validity evidence reported in the original study, that measures with more validity evidence in the original study had stronger psychometric properties in the replication sample, and that translated versions of scales had lower reliability.We discuss the implications of these findings for interpreting replication results, and make recommendations to improve measurement practices in future replications.
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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 | 0.570 | 0.847 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".