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Record W2887117156 · doi:10.7202/1050915ar

Utiliser le coefficient omega de McDonald à la place de l’alpha de Cronbach

2018· article· fr· W2887117156 on OpenAlexaffvenue
Sébastien Béland, Denis Cousineau, Nathalie Loye

Bibliographic record

VenueMcGill Journal of Education / Revue des sciences de l éducation de McGill · 2018
Typearticle
Languagefr
FieldPsychology
TopicCognitive and psychological constructs research
Canadian institutionsUniversity of OttawaUniversité de Montréal
Fundersnot available
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

Des chercheurs discutaient récemment de l’importance de rester à jour au sujet des plus récentes avancées en méthodes quantitatives. À ce titre, de nombreux auteurs ont exposé leur souhait de voir les chercheurs abandonner le populaire coefficient alpha de Cronbach. C’est dans une optique de diffusion et de vulgarisation que ce court article a comme objectif de présenter l’alternative qui semble la plus prometteuse pour mesurer la fidélité d’un test, le coefficient omega de McDonald, qui est basée sur l’analyse factorielle à un facteur commun.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.037
metaresearch head score (Gemma)0.215
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.037
Threshold uncertainty score0.196

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0370.215
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0060.008
Science and technology studies0.0020.004
Scholarly communication0.0040.005
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.002

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 head0.428
GPT teacher head0.506
Teacher spread0.078 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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

Citations98
Published2018
Admission routes2
Has abstractyes

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Same venueMcGill Journal of Education / Revue des sciences de l éducation de McGillSame topicCognitive and psychological constructs researchFrench-language works237,207