Confirmatory factor analyses of the WISC-IV Spanish core and supplemental subtests: Validation evidence of the Wechsler and CHC models
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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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 | 0.056 | 0.139 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".