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Record W2974010296 · doi:10.1044/2019_jslhr-l-18-0411

Validity Evidence for the LittlEARS Early Speech Production Questionnaire: An English-Speaking, Canadian Sample

2019· article· en· W2974010296 on OpenAlexaffabout
Olivia Daub, Janis Oram Cardy, Andrew M. Johnson, Marlene Bagatto

Bibliographic record

VenueJournal of Speech Language and Hearing Research · 2019
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsWestern University
Fundersnot available
KeywordsPsychologySpoken languageTest (biology)Language developmentExpressive languageScale (ratio)Speech productionLanguage productionTest validityConcurrent validityDevelopmental psychologyAudiologyPsychometricsInternal consistencyLinguisticsNatural language processingComputer scienceMedicineCognition

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.390
Threshold uncertainty score0.972

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.158
GPT teacher head0.434
Teacher spread0.275 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2019
Admission routes2
Has abstractyes

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