Augmenting the Core Battery With Supplementary Subtests: Wechsler Adult Intelligence Scale—IV Measurement Invariance Across the United States and Canada
Bibliographic record
Abstract
Examination of measurement invariance provides a powerful method to evaluate the hypothesis that the same set of psychological constructs underlies a set of test scores in different populations. If measurement invariance is observed, then the same psychological meaning can be ascribed to scores in both populations. In this study, the measurement model including core and supplementary subtests of the Wechsler Adult Intelligence Scale-Fourth edition (WAIS-IV) were compared across the U.S. and Canadian standardization samples. Populations were compared on the 15 subtest version of the test in people aged 70 and younger and on the 12 subtest version in people aged 70 or older. Results indicated that a slightly modified version of the four-factor model reported in the WAIS-IV technical manual provided the best fit in both populations and in both age groups. The null hypothesis of measurement invariance across populations was not rejected, and the results provide direct evidence for the generalizability of convergent and discriminant validity studies with the WAIS-IV across populations. Small to medium differences in latent means favoring Canadians highlight the value of local norms.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 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".