Using estimated factor scores from a bifactor analysis to examine the unique effects of the latent variables measured by the WAIS-IV on academic achievement.
Bibliographic record
Abstract
This study used estimated factor scores from a bifactor analysis of the Wechsler Adult Intelligence Scale-Fourth Edition (WAIS-IV) to examine the unique effects of its latent variables on academic achievement. In doing so, we addressed the potential limitation of multicollinearity in previous studies of the incremental validity of the WAIS-IV. First, factor scores representing psychometric g and 4 group factors representing the WAIS-IV index scales were computed from a bifactor model. Subtest and composite scores for the Wechsler Individual Achievement Test-Third Edition (WIAT-II) were then predicted from these estimated factor scores in simultaneous multiple regression. Results of this study only partially replicated the findings of previous research on the incremental validity of scores that can be derived from performance on the WAIS-IV. Although we found that psychometric g is the most important underlying construct measured by the WAIS-IV for the prediction of academic achievement in general, results indicated that the unique effect of Verbal Comprehension is also important for predicting achievement in reading, spelling, and oral communication skills. Based on these results, measures of both psychometric g and Verbal Comprehension could be cautiously interpreted when considering high school students' performance in these areas of achievement.
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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 | 0.016 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".