A Validity-Based Framework for Understanding Replication in Psychology
Bibliographic record
Abstract
In recent years, psychology has wrestled with the broader implications of disappointing rates of replication of previously demonstrated effects. This article proposes that many aspects of this pattern of results can be understood within the classic framework of four proposed forms of validity: statistical conclusion validity, internal validity, construct validity, and external validity. The article explains the conceptual logic for how differences in each type of validity across an original study and a subsequent replication attempt can lead to replication "failure." Existing themes in the replication literature related to each type of validity are also highlighted. Furthermore, empirical evidence is considered for the role of each type of validity in non-replication. The article concludes with a discussion of broader implications of this classic validity framework for improving replication rates in psychological 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 | 0.617 | 0.662 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.017 | 0.011 |
| Science and technology studies | 0.007 | 0.089 |
| Scholarly communication | 0.015 | 0.025 |
| Open science | 0.011 | 0.016 |
| Research integrity | 0.013 | 0.016 |
| 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".