Psychometric Properties and Standardization Samples of Four Screening Tests for Infants and Young Children: A Review
Bibliographic record
Abstract
In Brief Purpose: This article compares traditional psychometric properties (interrater and test-retest reliability, concurrent and predictive validity), clinical epidemiological characteristics (sensitivity, specificity, and positive predictive values), and standardization samples of four tests useful to pediatric therapists in screening infants and young children for developmental delays. Summary of Key Points: Pediatric therapists are often involved in screening infants and young children for developmental delay. Ideally, they will use standardized tests that have strong psychometric properties (eg, reliability, validity, sensitivity, specificity). The four tests described in this article vary in meeting these criteria. They vary as well in the domains assessed, age ranges for which they are intended, and desired qualifications of the examiners. Conclusions: Each of the four tests reviewed has identified strengths and weaknesses. Practicing clinicians should select screening tests based on the test’s stated purpose, qualifications of the examiner, age range covered, administration and scoring time, developmental domains encompassed, comparability of the standardization sample, and strength of the test’s psychometric properties. This review of the psychometric properties of four screening tests: Ages and Stages Questionnaires (ASQ), the AIMS, the Denver-II, and the Harris Infant Neuromotor Test (HINT) serves as a reminder that therapists need to understand psychometric properties, clinical epidemiological characteristics, and characteristics of the standardization samples when selecting a screening test for clinical use.
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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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".