Revealing Cultural Dynamics in WAIS-IV Performance: a Comparative Analysis of Age Cohorts in Taiwanese and U.S. Populations
Bibliographic record
Abstract
OBJECTIVE: This study examined the relationship between culture and cognitive abilities by comparing WAIS-IV subtests, index scores, and Full-Scale Intelligence Quotient (FSIQ) scores across various age groups in the Taiwanese and U.S. populations. METHOD: The Taiwanese and U.S. versions of WAIS-IV are comprehensively compared, examining subtest items, psychometrics, and sample characteristics. Scaled scores are compared by extracting raw scores with a scaled score of 10 from each subtest scale in the Taiwanese version and applying U.S. age norms to acquire U.S. scaled scores. RESULTS: Despite the mean FSIQ score closely aligning with the U.S. sample, notable discrepancies are evident in the Taiwanese Verbal Comprehension Index (VCI) score, potentially influenced by cultural fairness of the tests. Significant variations are observed among age cohorts in the Taiwanese sample, with younger individuals excelling in Processing Speed Index, Working Memory Index, Perceptual Reasoning Index, and FSIQ, while maintaining comparable VCI scores to their U.S. counterparts. Conversely, older cohorts demonstrate lower performance across various domains, except for visuospatial reasoning and organizational skills, compared to their U.S. counterparts. These subtest variations robustly correlate with educational disparities between the Taiwanese and U.S. samples. CONCLUSIONS: Despite the similarity in factor structures between the Taiwanese and U.S. versions of WAIS-IV, this study reveals cultural bias in both verbal and non-verbal subtests. The study highlights the intricate interplay among cognitive processing styles, cultural influences, and educational factors contributing to performance disparities.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".