PROFESSIONAL ISSUES: TEST DEVELOPMENT AND METHODSB-102Comparing Canadian and American Normative Scores on the Wechsler Intelligence Scale for Children-Fourth Edition
Bibliographic record
Abstract
Objective: The purpose of this study was to compare the interpretive effects of applying American versus Canadian normative systems for younger children using the Wechsler Intelligence Scale for Children (WISC-IV). Method: A large sample (N = 300) of children in grade 7 were administered the WISC-IV as part of a learning disability evaluation. All protocols were scored using both the Canadian and American normative data. Results: Statistically significant differences were found between the obtained IQ, Index and subtests scores when the Canadian as opposed to the American normative systems were applied. The effect sizes of the differences, however, were small to medium. The average FSIQ using the American norms was 93.8 (SD = 10.6) compared to 90.2 (SD = 10.3) using the Canadian norms. The largest difference in index scores was found in the Working Memory Index score (Cohen's d = .33). For individual subtests, the largest differences were on Letter Number Sequencing (d = .38), and Comprehension (d = .35). The smallest differences were on Coding (d= -.04), Picture Completion (d = −.04), and Matrix Reasoning (d = −.08). Percentage agreement in normative classifications, defined as American and Canadian index scores within 5 points or within the same classification range, was as follows: FSIQ = 81.5%, GAI = 88.6%, VCI = 81.6%, PRI = 94.6%, WMI = 79.6%, and PSI = 97.7%. Conclusion: Contrary to recent findings regarding the WAIS-IV, the Canadian norms for the WISC-IV do not systematically reduce scores relative to American-derived scores. While significant differences were found, the effects were small and rarely meaningful. Only 1/5 of Canadian children with specific learning disabilities and/or ADHD obtained FSIQ or GAI scores that changed classification when accounting for measurement error.
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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 | 0.019 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 0.019 |
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".