Measurement equivalence of neuropsychological tests across education levels in older adults
Bibliographic record
Abstract
OBJECTIVE: The objective was to determine whether neuropsychological tests provide an equivalent measure of the same psychological constructs in older adults with low versus higher levels of education. METHOD: Confirmatory factor analysis was used to evaluate the fit of a three-factor model (Verbal Ability, Visuospatial Ability, Long-Term Retention) to scores from the neuropsychological battery of the Canadian Study of Health and Aging (CSHA). Measurement equivalence of the model across lower educated (LE; ≤8 years) and higher educated (HE; ≥9 years) participants was evaluated using invariance testing. RESULTS: The measurement model demonstrated adequate fit across LE and HE samples but the loadings of the 11 tests onto the three factors could not be constrained equally across groups. Animal Fluency and the Token Test were identified as noninvariant tests of Verbal Ability that, when freed from constraints, produced a partial metric invariance model. Scalar invariance testing identified the Buschke Cued Recall Test and Block Design as measures with invariant factor loadings but noninvariant intercepts. Analyses were replicated in age- and sex-matched subsamples. CONCLUSIONS: Metric and scalar invariance across HE and LE samples was achieved for seven of the 11 tests in the CSHA battery. Animal Fluency and the Token Test were noninvariant measures of Verbal Ability, suggesting that cognitive processes underlying performance on these tests may vary as a function of education. In addition, scores from Block Design and the Buschke Cued Recall Test were observed to differ in their scale of measurement between HE and LE examinees.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".