Construct validity and the validity of replication studies: A systematic review
Bibliographic record
Abstract
Currently there is little guidance for navigating measurement challenges that threaten construct validity in replication research. To identify common challenges and ultimately strengthen replication research, we conducted a systematic review of the measures used in the 100 original and replication studies from the Reproducibility Project Psychology (Open Science Collaboration, 2015). Results indicate that it was common for scales used in the original studies to have little or no validity evidence. Our systematic review demonstrates and corroborates evidence that issues of construct validity are sorely neglected in original and replicated research. We identify four measurement challenges replicators are likely to face: a lack of essential measurement information, a lack of validity evidence, measurement differences, and translation. Next, we offer solutions for addressing these challenges that will improve measurement practices in original and replication research. Finally, we close with a discussion of the need to develop measurement methodologies for the next generation of replication research.Public Significance: Over the past decade psychologists have been calling for methodological reform to increase the rigor and replicability of psychological science, which has been accompanied by progress in improving transparency and statistical practices. This paper presents rigorous measurement practices as foundational for generating knowledge from psychological science that can be translated to inform policy, develop interventions, and improve people’s lives. We review one of the largest sets of replication studies ever conducted to understand how measurement can be improved and develop measurement practices for the next generation of replication research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; a candidate call from one teacher head, not a consensus.
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".