Psychometric Assessment and Reporting Practices
Bibliographic record
Abstract
The aim of the current study is twofold: (a) to investigate the rates at which researchers assess and report on the psychometric properties of the measures they use in their research and (b) to examine whether or not researchers appear to be generally employing sound/unsound rationales when it comes to how they conduct test evaluations. Based on a sample of 368 articles published in four journals in the year 2004, the findings suggest that, although evidence bearing on score precision/reliability and the internal structure of item responses remains under-reported, researchers appear to be assessing the relationships between test scores and external variables relatively more frequently than in the past. However, findings also indicate that, all told, very few researchers are assessing and reporting on internal score validity, and score precision/reliability, and external score validity, and in that sequence, suggesting that applied researchers may not always be adopting sound test-evaluative rationales in their psychometric assessments.
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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 | 0.614 | 0.828 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.027 | 0.028 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.007 |
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".