Utility and Validity of Authentic Assessments and Conventional Tests for International Early Childhood Intervention Purposes: Evidence from U.S. National Social Validity Research
Bibliographic record
Abstract
The reported U.S. incidence of delay/disability in young children, and thus need for services, is far higher than those currently receiving early intervention supports and services [1]. Government representatives and policymakers in the U.S. have concluded that traditional tests fail to capture sufficient numbers of young children who must access early intervention supports at a critical life moment [2], even though state regulations often mandate their use. The early intervention field regards authentic assessments as a more effective alternative. However, few U.S. studies have been conducted to compare and validate the use of either conventional tests or authentic assessments for early intervention purposes. National social validity research in the United States by Bagnato et al. [3,4] revealed that authentic assessments fulfill the qualities/needs of the early childhood intervention field better than conventional tests. However, no national studies had been conducted to examine the qualities and patterns of use for authentic and conventional measures among interdisciplinary professionals. Based on an expanded national internet survey in the current follow-up research, we compared the qualities/patterns of use among professionals for both types of measures in the early childhood intervention field. Overall, median ratings indicate that the top authentic assessments are more valid/useful than the most popular conventional tests to accomplish most early intervention purposes. Based on the results, we share the implications as “practice-based research evidence” to guide international policymakers, professionals, and parents to advocate, choose, and use “best measures for best practices.”
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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 | 0.122 | 0.310 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".