Confirmatory factor analyses of the WISC-IV Spanish core and supplemental subtests: Validation evidence of the Wechsler and CHC models
Why this work is in the frame
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Bibliographic record
Abstract
The present study examined the factor structure of the Wechsler Intelligence Scale for Children–Fourth Edition, Spanish (WISC–IV Spanish, Wechsler, 2005a) with normative sample participants aged 6–16 years (N = 500) using confirmatory factor analytic techniques not reported in the WISC–IV Spanish Manual (Wechsler, 2005b). For the 10 core subtest configuration, 1 through 4, first-order factor models, and higher-order versus bifactor models were compared using confirmatory factor analyses. The correlated four-factor Wechsler model provided good fit to these data, but the bifactor model showed statistically significant improvement over the higher-order model and correlated four-factor model. For the 14 core and supplemental subtest configuration, an alternative five-factor model based upon Cattell-Horn-Carroll (CHC; as per Weiss, et al., 2013b) configuration was also estimated. Results indicated that for the 14 subtest configuration, the alternative CHC model was preferred to the four-factor Wechsler model and the bifactor version of the CHC model also fit these data best. Across both configurations, variance apportionment and model-based reliability estimates illustrate well the dominance of the general intelligence factor when compared to the influence of the various combinations of group factors. Implications for clinical interpretation and the anticipated revision of the measurement instrument are discussed.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it