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Record W2090101614 · doi:10.1177/0734282912449449

The Measurement Invariance of the Wong and Law Emotional Intelligence Scale (WLEIS) Across Three Chinese University Student Groups From Canada and China

2012· article· en· W2090101614 on OpenAlex
Tongwei Li, Donald H. Saklofske, Stephen C. Bowden, Gonggu Yan, Tak Fung

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Psychoeducational Assessment · 2012
Typearticle
Languageen
FieldPsychology
TopicEmotional Intelligence and Performance
Canadian institutionsUniversity of CalgaryWestern University
Fundersnot available
KeywordsMeasurement invariancePsychologyEmotional intelligenceEquivalence (formal languages)BeijingChinaScale (ratio)Social psychologyConfirmatory factor analysisMathematics educationStatisticsMathematicsStructural equation modelingPure mathematicsLaw

Abstract

fetched live from OpenAlex

The current study assessed the measurement equivalence of the Wong & Law Emotional Intelligence Scale (WLEIS) with three groups of Chinese university students. Two research sessions were conducted—one in Beijing, China with university students ( N = 680), and the other in Calgary, Canada where two groups of Chinese students were administered the WLEIS in either Chinese ( N = 151) or English ( N = 151). The WLEIS had satisfactory reliability, the four-factor structure was replicated, and metric invariance was supported across the three groups. The present study provided empirical support to the growing emotional intelligence measurement invariance literature and demonstrated the robustness of both the English and Chinese versions of the WLEIS.

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.

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)0.001
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 score0.106
Threshold uncertainty score0.970

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.033
GPT teacher head0.356
Teacher spread0.323 · 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