The BNT-38: Applying Rasch Analysis to Adapt the Boston Naming Test for Use With English and French Monolinguals and Bilinguals
Bibliographic record
Abstract
Purpose Currently, there is no reliable instrument to measure naming abilities in bilingual speakers of English and French. The Boston Naming Test (BNT; Kaplan, Goodglass, & Weintraub, 1983 )is a widely used scale for clinical assessments of language function, but it is not suitable to assess bilinguals. Rasch analysis provides a unique and powerful method to establish measurement invariance across language groups that improves reliability of measurement. Method Rasch analysis was applied to a sample ( n = 215) of English or French monolingual and bilingual speakers completing the BNT in either language. Participants included English-French bilinguals ( n = 83), English monolinguals ( n = 72), and French monolinguals ( n = 60). Results The best overall Rasch model fit was obtained after the removal of 22 misfitting items, resulting in a 38-item BNT solution (BNT-38), with a modification of 7 items that showed differential item functioning by language factor. To increase the clinical utility of the BNT-38 in French speakers and bilinguals, we generated ordinal-to-interval conversion tables for monolinguals and bilinguals in English and French. Conclusions Use of the BNT-38 and the associated conversion tables will allow valid comparisons of naming abilities across bilingual and monolingual English and French speakers of different age groups. Applying these tools increases accuracy in measurement of naming ability and higher diagnostic precision in French and English monolinguals and bilinguals.
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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 | 0.026 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".