Measurement Invariance of Early Development Instrument (EDI) Domain Scores Across Gender and ESL Status
Bibliographic record
Abstract
The Early Development Instrument (EDI) is a widely used teacher rating tool to assess kindergartners’ developmental outcomes in Canada and a number of other countries. This paper examines the measurement invariance of EDI domains across ESL status and gender by means of multi-group confirmatory factor analysis. The results suggest evidence of measurement invariance for physical health and well-being, social competence, emotional maturity and language and cognitive development domains. Moreover, the communication skills and general knowledge domain did not show acceptable fit in terms of RMSEA. The results and potential explanations are discussed. L'instrument de mesure du développement de la petite enfance (IMDPE) est un outil d’évaluation largement utilisée pour mesurer le développement des élèves en maternelle au Canada et dans d’autres pays. Cet article porte sur l’équivalence de mesure des domaines de l’IMDPE entre le statut d’ALS et le sexe par une analyse factorielle confirmatoire multigroupe. Les résultats font ressortir des preuves d’équivalence de mesure pour les domaines de la santé physique et le bienêtre, la compétence sociale, la maturité affective, le développement langagier et cognitif. De plus, le domaine des compétences en communication et des connaissances générales n’a pas démontré une correspondance acceptable par rapport à l'erreur quadratique moyenne de l'approximation (RMSEA). Nous discutons des résultats et proposons des explications possibles.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".