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Record W2010366672 · doi:10.1080/10409280701610838

Does the EDI Measure School Readiness in the Same Way Across Different Groups of Children?

2007· article· en· W2010366672 on OpenAlexaff
Martin Guhn, Anne Gadermann, Bruno D. Zumbo

Bibliographic record

VenueEarly Education and Development · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsUniversity of British Columbia
FundersAmerican Educational Research Association
KeywordsPsychologyDifferential item functioningDevelopmental psychologyContrast (vision)PopulationLanguage developmentPsychometricsItem response theoryDemography

Abstract

fetched live from OpenAlex

The present study investigates whether the Early Development Instrument (Offord & Janus, 1999 Offord, D., Janus, M. (1999). Early Development Instrument. A population-based measure for communities (2004/05 version). Retrieved November 20, 2006 www.offordcentre.com/readiness/EDI_viewonly.html [Google Scholar]) measures school readiness similarly across different groups of children. We employ ordinal logistic regression to investigate differential item functioning, a method of examining measurement bias. For 40,000 children, our analysis compares groups according to gender, English-as-a-second-language (ESL) status, and Aboriginal status. Our results indicate no systematic measurement differences regarding Aboriginal status and gender, except for 1 item on which boys are more likely than girls to be rated as physically aggressive by Kindergarten teachers. In contrast, ESL children systematically receive lower ratings on items of the language and communication domains—as expected by definition of ESL status—but not within the physical, social, and emotional domains. We discuss how our results fit with child development research and the purpose of the Early Development Instrument, thus supporting its validity.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.318
Threshold uncertainty score0.568

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.300
Teacher spread0.286 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations96
Published2007
Admission routes1
Has abstractyes

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