The Early Development Instrument: an evaluation of its five domains using Rasch analysis
Bibliographic record
Abstract
BACKGROUND: Early childhood development is a multifaceted construct encompassing physical, social, emotional and intellectual competencies. The Early Development Instrument (EDI) is a population-level measure of five domains of early childhood development on which extensive psychometric testing has been conducted using traditional methods. This study builds on previous psychometric analysis by providing the first large-scale Rasch analysis of the EDI. The aim of the study was to perform a definitive analysis of the psychometric properties of the EDI domains within the Rasch paradigm. METHODS: Data from a large EDI study conducted in a major Irish urban centre were used for the analysis. The unidimensional Rasch model was used to examine whether the EDI scales met the measurement requirement of invariance, allowing responses to be summated across items. Differential item functioning for gender was also analysed. RESULTS: Data were available for 1344 children. All scales apart from the Physical Health and Well-Being scale reliably discriminated between children of different levels of ability. However, all the scales also had some misfitting items and problems with measuring higher levels of ability. Differential item functioning for gender was particularly evident in the emotional maturity scale with almost one-third of items (9 out of 30) on this scale biased in favour of girls. CONCLUSION: The study points to a number of areas where the EDI could be improved.
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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 | 0.053 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".