Bibliographic record
Abstract
Cronbach and Meehl (1955) introduced the concept of construct validity and described how researchers can demonstrate that their measures have construct validity. Although the term construct validity is widely used, few researchers follow Cronbach and Meehl’s recommendation to quantify construct validity with the help of nomological networks. As a result, the construct validity of many popular measures in psychology is unknown. I call for rigorous tests of construct validity that follow Cronbach and Meehl’s recommendations to improve psychology as a science. Without valid measures even replicable results are uninformative. I suggest that a proper program of validation research requires a multi-method approach and causal modeling of correlations with structural equation models. Construct validity should be quantified to enable cost-benefit analyses and to replace existing measures with better measures that have superior construct validity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.331 | 0.399 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.007 | 0.082 |
| Scholarly communication | 0.014 | 0.035 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.013 | 0.027 |
| Insufficient payload (model declined to judge) | 0.003 | 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".