Item response theory analyses of the Cambridge Face Memory Test (CFMT).
Bibliographic record
Abstract
We evaluated the psychometric properties of the Cambridge Face Memory Test (CFMT; Duchaine & Nakayama, 2006). First, we assessed the dimensionality of the test with a bifactor exploratory factor analysis (EFA). This EFA analysis revealed a general factor and 3 specific factors clustered by targets of CFMT. However, the 3 specific factors appeared to be minor factors that can be ignored. Second, we fit a unidimensional item response model. This item response model showed that the CFMT items could discriminate individuals at different ability levels and covered a wide range of the ability continuum. We found the CFMT to be particularly precise for a wide range of ability levels. Third, we implemented item response theory (IRT) differential item functioning (DIF) analyses for each gender group and 2 age groups (age ≤ 20 vs. age > 21). This DIF analysis suggested little evidence of consequential differential functioning on the CFMT for these groups, supporting the use of the test to compare older to younger, or male to female, individuals. Fourth, we tested for a gender difference on the latent facial recognition ability with an explanatory item response model. We found a significant but small gender difference on the latent ability for face recognition, which was higher for women than men by 0.184, at age mean 23.2, controlling for linear and quadratic age effects. Finally, we discuss the practical considerations of the use of total scores versus IRT scale scores in applications of the CFMT.
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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 | 0.015 | 0.091 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".