Percent Grammatical Utterances Between 4 and 9 Years of Age for the Edmonton Narrative Norms Instrument: Reference Data and Psychometric Properties
Bibliographic record
Abstract
Purpose The purpose of this article was to provide the reference data and evaluate psychometric properties for the percent grammatical utterances (PGU; Eisenberg & Guo, 2013 ) 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 were 377 children who were between 4 and 9 years of age, including 300 children with typical language (TL) and 77 children with language impairment (LI). Narrative samples were collected using the ENNI protocol (i.e., a story generation task). PGU was computed from the samples. Split-half reliability, concurrent criterion validity, and diagnostic accuracy for PGU were further evaluated. Results PGU increased significantly in children between 4 and 9 years of age in both the TL and LI groups. In addition, the correlation coefficients for the split-half reliability and concurrent criterion validity of PGU were all large ( r s ≥ .557, p s < .001). The diagnostic accuracy of PGU was also good or acceptable from ages 4 to 9 years. Conclusions With the attested psychometric properties, PGU computed from the ENNI could be used as an assessment tool for identifying children with LI between 4 and 9 years of age. The reference data of PGU could also be used for monitoring treatment progress. Supplemental Material https://doi.org/10.23641/asha.9630590
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".