Partial measurement equivalence of French and English versions of the Canadian Study of Health and Aging neuropsychological battery
Bibliographic record
Abstract
Neuropsychological batteries are often translated for use across populations differing in preferred language. Yet, equivalence in construct measurement across groups cannot be assumed. To address this issue, we examined data from the Canadian Study of Health and Aging, a large study of older adults. We tested the hypothesis that the latent variables underlying the neuropsychological battery administered in French or English were the same (invariant). The best-fitting baseline model, established in the English-speaking Exploratory sample (n = 716), replicated well in the English-speaking Validation sample (n = 715), and the French-speaking sample (FS, n = 446). Across the English- and FSs, two of the factors, Long-term Retrieval and Visuospatial speed, displayed invariance, that is, reflected the same constructs measured in the same scales. In contrast, the Verbal Ability factor showed only partial invariance, reflecting differences in the relative difficulty of some tests of language functions. This empirical demonstration of partial measurement invariance lends support to the continued use of these translated measures in clinical and research contexts and illustrates a framework for detailed evaluation of the generality of models of cognition and psychopathology, across groups of any sort.
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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 | 0.019 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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 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".