Psychometric properties of PGU (Guo et al., 2019)
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 (rs ≥ .557, ps < .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 S1. Computation of PGU. Supplemental Material S2. Frequency and percentage of each error type and number and percentage of children who produced each error type by language status and age. Supplemental Material S3. F values, p values, and effect sizes (d) for the group differences in total number of C-units, mean length of C-units in morphemes (MLCUm), number of different words (NDW), and number of C-units for the PGU analysis (# of CU for PGU) by age. Supplemental Material S4. Z score and confidence interval calculation. Supplemental Material S5. Indices of diagnostic accuracy for PGU using prescriptive cutoff scores. Guo, L.-Y., Eisenberg, S., Schneider, p., & Spencer, L. (2019). Percent grammatical utterances between 4 and 9 years of age for the Edmonton Narrative Norms Instrument: Reference data and psychometric properties. American Journal of Speech-Language Pathology, 28(4), 1448–1462. https://doi.org/10.1044/2019_AJSLP-18-0228
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".