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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".