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Record W2982537750 · doi:10.5430/jct.v8n4p1

An Investigation of the Psychometric Properties of Emotional Intelligence Scale Using Item Response Theory

2019· article· en· W2982537750 on OpenAlexvenueno aff
Said Aldhafri, Yousef Abu Shindi

Bibliographic record

VenueJournal of Curriculum and Teaching · 2019
Typearticle
Languageen
FieldPsychology
TopicEmotional Intelligence and Performance
Canadian institutionsnot available
FundersSultan Qaboos University
KeywordsItem response theoryEmotional intelligencePsychologyExploratory factor analysisScale (ratio)Goodness of fitTraitConstruct (python library)PsychometricsSocial psychologyDevelopmental psychologyStatisticsComputer scienceMathematics

Abstract

fetched live from OpenAlex

Students’ emotional intelligence represents an important variable that is connected to students’ academic achievementand life success. One main challenge when measuring students’ emotional intelligence is to have a valid and reliablemeasure that captures this emotional construct. The current study aims to investigate the psychometric properties of theArabic version of Alsmadoni emotional intelligence scale (AEIS-25) using item response theory (IRT) models. Thestudy was applied among 3030 students in grades 7-10 in Oman.Data model fit was examined through evaluating IRT assumptions (i.e. unidimensional assumption and localindependence assumption) and goodness of fit (i.e. items fit and persons fit). It was found that item parameters wereacceptable and satisfactory, which indicates the appropriateness of AEIS to examine emotional intelligence amongadolescents. Findings from exploratory factor analysis for 2924 students and for the remaining 24 items indicated thepresence of a 2-factor model since the third factor was loaded by only one item. AEIS was considered as a reliable andpotentially valid measure of trait emotional intelligence.

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)0.002
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 score0.037
Threshold uncertainty score0.208

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.043
GPT teacher head0.329
Teacher spread0.285 · 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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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