Analyses traditionnelles et FDI des échelles de mesure: application à l‘échelle de l'intensité du raisonnement cognitif
Bibliographic record
Abstract
Abstract Traditional scale purification methods and IRT analyses are applied to a Chinese version of the short Need for Cognition scale (18 items). Traditional methods indicate that the scale contains one clear dominant factor (α = 0.822). Nevertheless, it is also indicated that the composite contains poor items, which should perhaps be deleted from the scale. This is made particularly evident by the presence of low factor loadings. On the other hand, IRT analyses identify anomalies with individual scale items that are not made apparent with traditional methods. In particular, some items do not tend to discriminate well between individuals who score highly or poorly on Need for Cognition. Résumé Les méthodes traditionnelles d'épuration ainsi qu'une approche basée sur le fonctionnement différentiel de l'ltem (FDI) sont comparées lors de l'analyse psychométrique d'une version chinoise de l'échelle de l'intensité du raisonnement cognitif réduite à 18 items. D'une part, les résultats rapportés par l'approche traditionnelle démontrent qu'un facteur dominant est clairement identifié (α = 0.822). Néanmoins, le composé qui en résulte renferme des items ayant des saturations relativement faibles. Ceci indique done que certains de ces items devraient être potentiellement éliminés. D'autre part, l'analyse FDI révèle des faiblesses de certains items de cette échelle non décelables par les procédures traditionnelles. Parmi ces faiblesses, nous pouvons citer la capacité discriminatoire réduite entre des individus présentant unfaible ou un grand niveau de l'intensité du raisonnement cognitif.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads 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".