Finite Verb Morphology Composite Between Age 4 and Age 9 for the Edmonton Narrative Norms Instrument: Reference Data and Psychometric Properties
Bibliographic record
Abstract
Purpose The purpose of this study was to provide reference data and evaluate the psychometric properties for the finite verb morphology composite (FVMC) measure in children between 4 and 9 years of age from the database of the Edmonton Narrative Norms Instrument (ENNI; Schneider, Dubé, & Hayward, 2005 ). Method Participants included 377 children between age 4 and age 9, including 300 children with typical language and 77 children with language impairment (LI). Narrative samples were collected using a story generation task. FVMC scores were computed from the samples. Split-half reliability, concurrent criterion validity, and diagnostic accuracy for FVMC were further evaluated. Results Children's performance on FVMC increased significantly between age 4 and age 9 in the typical language and LI groups. Moreover, the correlation coefficients for the split-half reliability and concurrent criterion validity of FVMC were medium to large ( r s ≥ .429, p s < .001) at each age level. The diagnostic accuracy of FVMC was good or acceptable from age 4 to age 7, but it dropped to a poor level at age 8 and age 9. Conclusion With the empirical evidence, FVMC is appropriate for identifying children with LI between age 4 and age 7. The reference data of FVMC could also be used for monitoring treatment progress. Supplemental Material https://doi.org/10.23641/asha.10073183
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".