Invariance of the Measurement Model Underlying the Wechsler Adult Intelligence Scale-IV in the United States and Canada
Bibliographic record
Abstract
A measurement model describes both the numerical and theoretical relationship between observed scores and the corresponding latent variables or constructs. Testing a measurement model across groups is required to determine if the tests scores are tapping the same constructs so that the same meaning can be ascribed to the scores. Contemporary tests of intelligence describe a number of closely related cognitive abilities, each ability being sampled by a set of observed scores. This study examined the invariance of the measurement model underlying the Wechsler Adult Intelligence Scale—IV (WAIS-IV) in the U.S. and the Canadian standardization samples. The model satisfied the assumption of invariance across samples with subtest scores reflecting similar construct measurement in both samples. Consistent with previous research with the WAIS-III, slightly higher latent variable means were found in the Canadian WAIS-IV normative sample. The results demonstrate the generality of construct validity in measurement of cognitive abilities across U.S. and Canadian samples and highlight the importance 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 | 0.017 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".