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Record W6907450231 · doi:10.23641/asha.10073183

Psychometric properties of FVMC (Guo et al., 2019)

2019· article· en· W6907450231 on OpenAlexaboutno aff

Bibliographic record

Venuefigshare ASHA Publications · 2019
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsReliability (semiconductor)Age groupsVerbReceiver operating characteristicCorrelationConcurrent validity

Abstract

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<b>Purpose: </b>The purpose of this study was to provide reference data and evaluate the psychometric properties for the finite verb morphology composite (FVMC) measure in children between 4 and 9 years of age from the database of the Edmonton Narrative Norms Instrument (ENNI; Schneider, Dubé, &amp; Hayward, 2005).<b>Method: </b>Participants included 377 children between age 4 and age 9, including 300 children with typical language and 77 children with language impairment (LI). Narrative samples were collected using a story generation task. FVMC scores were computed from the samples. Split-half reliability, concurrent criterion validity, and diagnostic accuracy for FVMC were further evaluated.<b>Results: </b>Children's performance on FVMC increased significantly between age 4 and age 9 in the typical language and LI groups. Moreover, the correlation coefficients for the split-half reliability and concurrent criterion validity of FVMC were medium to large (<i>r</i>s ≥ .429, <i>p</i>s &lt; .001) at each age level. The diagnostic accuracy of FVMC was good or acceptable from age 4 to age 7, but it dropped to a poor level at age 8 and age 9.<b>Conclusion: </b>With the empirical evidence, FVMC is appropriate for identifying children with LI between age 4 and age 7. The reference data of FVMC could also be used for monitoring treatment progress.<b><br></b><b>Supplemental Material S1. </b>Description of the stories in the ENNI protocol. <br><b>Supplemental Material S2. </b>Computation of the finite verb morphology composite (FVMC). <br><b>Supplemental Material S3.</b> Example of a receiver operating characteristic (ROC) curve analysis. <br><b>Supplemental Material S4. </b><i>F</i> values, <i>p</i> values, and effect sizes (<i>d</i>) 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 obligatory contexts for the FVMC analysis (# of OC for FVMC) by age.<br><b>Supplemental Material S5.</b> <i>Z</i> score and confidence interval calculation table for FVMC. <br>Guo, L.-Y., Eisenberg, S., Schneider, P., &amp; Spencer, L. (2019). Finite verb morphology composite between age 4 and age 9 for the Edmonton Narrative Norms Instrument: Reference data and psychometric properties. <i>Language, Speech, and Hearing Services in Schools, 51</i>(1)<i>, </i>128-143. https://doi.org/10.1044/2019_LSHSS-19-0028

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.784
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.1950.014

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 head0.062
GPT teacher head0.322
Teacher spread0.260 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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