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Pegnato Revisited: Using Discriminant Analysis to Identify Gifted Children

2004· article· en· W1537721295 sur OpenAlexaff
Michael C. Pyryt

Notice bibliographique

RevuePsychology science · 2004
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueDiverse Academic Research Areas
Établissements canadiensUniversity of Calgary
Organismes subventionnairesnon disponible
Mots-clésOperationalizationIdentification (biology)PsychologyTest (biology)Linear discriminant analysisGifted educationContext (archaeology)DiscriminantIntelligence quotientDevelopmental psychologyMathematics educationArtificial intelligenceComputer scienceCognition
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Abstract The purpose of this paper is to provide an overview of discriminant analysis and an illustrative example of how this powerful technique can maximize the and (Pegnato and Birch, 1959) of screening procedures for identifying intellectually gifted students. The best predictors of scores on an individually-administered intelligence test were scores on group IQ and achievement tests. Key words: Identification, Giftedness, Discriminant analysis Pegnato Revisited: Using Discriminant Analysis to Identify Gifted Children The identification of gifted students remains a controversial issue in gifted education (Feldhusen & Jarwan, 2000; Hany, 1993; Heller & Feldhusen, 1986; Jarwan & Asher, 1994). In addition to lack of agreement about the nature of giftedness, the practical issue of how to implement an identification system that combines multiple sources of information is a source of debate. In many instances, the definition and operationalization of giftedness is legislated. Although there are many ways that one can be gifted (Marland, 1971), giftedness is often operationalized solely in terms of an arbitrary IQ cut-off score on an individuallyadministered intelligence test. Since the cost of administering this test is very expensive, many school systems implement one or more screening procedures to determine which students should receive the individual intelligence test. Effectiveness and Efficiency A seminal article in the gifted education literature is Pegnato & Birch's (1959) report of the of screening approaches in gifted education, which was based on Pegnato's (1958) dissertation. Pegnato & Birch (1959) introduced the concepts of effectiveness and efficiency of screening procedures for identifying gifted children. In the context of a school system, the of a screening procedure is the ratio of students identified by a procedure to the total number of gifted students in the school system. Unless a school system administers the criterion measure to all of the students in the school system, the number of identified gifted students becomes the estimate for the total. The of a screening procedure is the ratio of the number of students identified by a screening procedure to the number of students referred by a screening procedure. Pegnato and Birch (1959) reported that teachers in their study were able to identify 41 of 91 intellectually gifted students (those with Stanford-Binet IQ scores above 136), resulting in an ratio of 45.1%. To find these 41 students, teachers had to nominate 154 students, resulting in an ratio of 41/154 or 26.6%. There is a trade-off between and efficiency. A group IQ cut-off score of 115 was found to be 92.3% effective in identifying intellectually gifted students. However, the score was only 18.7%. Raising the group IQ cut-off to 130 increased the ratio to 55.5% at the expense of effectiveness, which dropped to 21.9%. Preference for one approach depends upon a person's role in gifted education. Advocates for gifted children want to ensure that every gifted child is identified and served, so advocates are more concerned with effectiveness. Since the cost of administering individual intellectual assessments is expensive, school administrators and school psychologists lean toward efficiency. The purpose of this paper is to provide an overview of discriminant analysis and an illustrative example of how this technique can maximize the and of procedures for identifying intellectually gifted children. Discriminant Analysis Developed by Fisher (1936), discriminant analysis is a multiple regression technique that seeks to find the best linear weighting of predictor variables to maximize the differences among two or more groups. Variables that contribute most to the prediction of group membership in relation to other variables are given the highest weights. …

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,022
score de la tête « metaresearch » (Gemma)0,044
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,022
Score d'incertitude au seuil0,114

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0220,044
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0060,004
Études des sciences et des technologies0,0010,003
Communication savante0,0040,003
Science ouverte0,0010,004
Intégrité de la recherche0,0010,004
Charge utile insuffisante (le modèle a refusé de juger)0,0020,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,089
Tête enseignante GPT0,518
Écart entre enseignants0,429 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations11
Publié2004
Routes d'admission1
Résumé présentoui

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