Psychometric properties and factor structure of the Early Development Instrument in a sample of Jordanian children
Bibliographic record
Abstract
BACKGROUND: Investing in children's early years can have a lasting positive effect, such as better academic outcomes throughout their school careers. In Jordan, investments have been made in early childhood development and early childhood care and education to improve children's school readiness. School readiness comprises a range of abilities needed to succeed in school, including physical, emotional, social, and cognitive skills. To measure the impact of these investments on children's school readiness, Jordan has been implementing the Early Development Instrument (EDI), a population-level, teacher-completed checklist of children's school readiness, assessing children's development in five main areas, referred to as domains. METHODS: The goal of the current study was to examine the psychometric properties of the Arabic version of the EDI, using data collected in 2018 on a sample of 5952 children in Jordan. The EDI was translated from the original English version to Arabic and adapted for use in Jordan. We conducted a categorical confirmatory factor analysis (CFA) for each of the five domains of the EDI and examined the reliability of the domains and subdomains using Cronbach's alpha reliability coefficient. RESULTS: With few exceptions, the study results are in line with those of the analysis of the psychometric properties found with the original, Canadian English version of the EDI in a population of Canadian children. Results of CFAs demonstrated, for the most part, good model fits. Internal consistency indices of the domains ranged from 0.60 for physical health and well-being to 0.96 for social competence. For the subdomains, they ranged from 0.26 to 0.94. CONCLUSIONS: Our results provide empirical support for the adaptation of the EDI for population monitoring of school readiness in Jordan. Validation of the Arabic adaptation opens up the possibility of assessing school readiness of young children in Jordan in comparison to the many other countries that have successfully adapted and applied the EDI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".