Construct Validity of Raven's Advanced Progressive Matrices for African and Non‐African Engineering Students in South Africa
Bibliographic record
Abstract
We test the hypothesis that the Raven's Advanced Progressive Matrices has the same construct validity in African university students as it does in non‐African students by examining data from 306 highly select 17‐ to 23‐year olds in the Faculties of Engineering and the Built Environment at the University of the Witwatersrand (177 Africans, 57 East Indians, 72 Whites; 54 women, 252 men). Analyses were made of the Matrices scores, an English Comprehension test, the Similarities subscale from the South African Wechsler Adult Intelligence Scale, end‐of‐year university grades, and high‐school grade point average. Out of the 36 Matrices problems, the African students solved an average of 23; East Indian students, 26; and White students, 29 ( p <.001), placing them at the 60th, 71st, and 86th percentiles, respectively, and yielding IQ equivalents of 103, 108, and 118 on the 1993 US norms. The same pattern of group differences was found on the Comprehension Test, the Similarities subscale, university course grades, and high‐school grade‐point average. The items on the Matrices ‘behaved’ in the same way for the African students as they did for the non‐African students, thereby indicating the test's internal validity. Item analyses, including a confirmatory factor analysis, showed that the African/non‐African difference was most pronounced on the general factor of intelligence. Concurrent validity was demonstrated by correlating the Matrices with the other measures, both individually and in composite. For the African group, the mean r =.28, p <.05, and for the non‐African group, the mean r =.27, p <.05. Although the intercepts of the regression lines for the two groups were significantly different, their slopes were not. The results imply that scores on the Raven's Matrices are as valid for Africans as they are for non‐Africans.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".