The Developmental Assessment of Social Communication Ability (DASCA): initial creation and psychometric description
Bibliographic record
Abstract
OBJECTIVE: The dearth of tools to quantify and track growth in social communication ability has been a barrier to understanding and monitoring treatment outcomes for neurodevelopmental disorders. We undertook a multi-staged, multisite study to create the Developmental Assessment of Social Communication Ability (DASCA), a new measure explicitly developed as a clinical outcome assessment for monitoring change-both over the course of development and in response to treatment. METHODS: The DASCA is a caregiver-report instrument created using a mixed-methods approach. Qualitative components of this approach included focus groups and cognitive debriefing interviews. Quantitative components included dimensionality analysis, differential item functioning, and item response theory modeling. The item bank was iteratively refined to assess social communication skills that are typically acquired by early- to middle- childhood. RESULTS: The final DASCA item bank contains 184 items. Expressive language was a major factor in determining the appropriateness of some items for certain groups of children. Negligible differential item functioning, primarily by age, was observed for some items. However, impact analyses determined that this differential item functioning did not meaningfully impact overall scores. LIMITATIONS: Given that sample size limitations prevented us from using separate samples for exploratory and confirmatory phases of modeling, it will be important to gather additional validity evidence in independent samples, especially as the current data were collected during the COVID-19 pandemic. CONCLUSION: The DASCA holds promise as an outcome measure for assessing changes in social communication ability. Ongoing development efforts include creating a computer adaptive test administration to allow for serial assessments using different item sets to yield a consistent score that is sensitive to change.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".