Clausal Density Between Ages 4 and 9 Years for the Edmonton Narrative Norms Instrument: Reference Data and Psychometric Properties
Bibliographic record
Abstract
Purpose This study provided reference data and examined psychometric properties for clausal density (CD; i.e., number of clauses per utterance) in children between ages 4 and 9 years from the database of the Edmonton Narrative Norms Instrument (ENNI). Method Participants in the ENNI database included 300 children with typical language (TL) and 77 children with language impairment (LI) between the ages of 4;0 (years;months) and 9;11. Narrative samples were collected using a story generation task, in which children were asked to tell stories based on six picture sequences. CD was computed from the narrative samples. The split-half reliability, concurrent criterion validity, and diagnostic accuracy were evaluated for CD by age. Results CD scores increased significantly between ages 4 and 9 years in children with TL and those with LI. Children with TL produced higher CD scores than those with LI at each age level. In addition, the correlation coefficients for the split-half reliability and concurrent criterion validity of CD scores were all significant at each age level, with the magnitude ranging from small to large. The diagnostic accuracy of CD scores, as revealed by sensitivity, specificity, and likelihood ratios, was poor. Conclusions The finding on diagnostic accuracy did not support the use of CD for identifying children with LI between ages 4 and 9 years. However, given the attested reliability and validity for CD, reference data of CD from the ENNI database can be used for evaluating children's difficulties with complex syntax and monitoring their change over time. Supplemental Material https://doi.org/10.23641/asha.13172129.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".