Confirmatory Factor Analysis of the WAIS-IV and WMS-IV in Older Adults
Bibliographic record
Abstract
New editions of the Wechsler Adult Intelligence and Memory scales are now available. Yet, given the significant changes in these new releases and the skepticism that has met them, independent evidence on their psychometric properties is much needed but currently lacking. We administered the WAIS-IV and the Older Adult version of the WMS-IV to 145 older adults. We examined how closely our data matched the normative sample by comparing our scaled scores with those of the publisher and by evaluating interrelations among subtests using confirmatory factor analysis. Not surprisingly, scaled scores from our sample were somewhat higher than those from the normative sample on some tests. Factor analysis on our sample provided support for a higher-order model of the WAIS-IV/WMS-IV Older Adults battery combined. In addition, allowing some subtests to load on more than one factor significantly improved model fit. The best fitting model for our sample was also the best for the normative sample. Overall, the data suggest that the factor analysis models generated from the normative samples for the new WAIS-IV and WMS-IV are reliable.
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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 | 0.023 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".