Exploratory Factor Analysis of the Canadian Wechsler Intelligence Scale for Children-Fifth Edition for a Sample of First Nations Students
Bibliographic record
Abstract
Native Americans and First Nation students are overrepresented in special education and underrepresented in structural bias research of the intelligence measures that place them there. There are several empirical studies of test bias on the Wechsler scales due to their popularity within the school system, however there is little exploratory factor analysis research on these scales with the Native American Indian population. Further, the Native American Indian and First Nation population is a relatively small minority group compared to other racial and ethnic groups in North America and this group is underrepresented in government statistics and overlooked in funding for policies that provide prevention for several risk factors. This study aimed to discover the factor structure of the WISC-VCDN with First Nations students to provide understanding and better interpretation of scores to facilitate ethical data-based decision making and provision of special education services to First Nations students. A total of 102 participant data were collected and a replication of the Canivez, Watkins, and Dombrowski (2016) study was followed to ensure best practice of Exploratory Factor Analysis. Results indicated that a three-factor model was most viable for the First Nations students on the WISC-VCDN, which is dissimilar to previous research. However, results of the dominance of the general intelligence (g) factor was similar to previous research of the Wechsler scales using both methods of Exploratory and Confirmatory Factor Analysis. Future research directions and implications for First Nations students, data-based decision making, and special services eligibility is 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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".