Validity Evidence for the LittlEARS Early Speech Production Questionnaire: An English-Speaking, Canadian Sample
Bibliographic record
Abstract
Purpose This study reports validity evidence for an English translation of the LittlEARS Early Speech Production Questionnaire (LEESPQ). The LEESPQ was designed to support early spoken language outcome monitoring in young children who are deaf/hard of hearing. Methods Data from 90 children with normal hearing, ages 0–18 months, are reported. Parents completed the LEESPQ in addition to a concurrent measure of spoken language development, the Receptive-Expressive Emergent Language Test–Third Edition. Normal hearing status and development were confirmed. Results Traditional scale analyses, in addition to item parameters, are reported. The LEESPQ was highly correlated with the Receptive-Expressive Emergent Language Test–Third Edition ( r = .92) and age ( r = .90) and had high internal consistency (Ω = 0.92). Common factor analysis revealed 2 underlying factors conceptually mapping onto items measuring vocal and symbolic development. A latent traits model was the best fit to the data, and item difficulty broadly conformed to theoretical expectations. Conclusions The present work demonstrates that the LEESPQ accurately captures early spoken language development in a typically developing group of young children. The LEESPQ holds promise as a clinically feasible, spoken language outcome monitoring tool. Future work to identify differences in performance characteristics between typically developing children and clinical populations is warranted.
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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 | 0.013 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".