How do children and youth with autism spectrum disorder self-report on behavior? A study of the validity indexes on the Behavior Assessment System for Children, Second Edition, self-report of personality
Notice bibliographique
Résumé
Self-report measures offer a unique source of information in the assessment and intervention of individuals with autism spectrum disorder. However, it is not known if children with autism spectrum disorder can answer self-report questionnaires accurately and consistently. As a step to address this issue, we examined validity indexes of the Behavior Assessment System for Children, Second Edition, self-report of personality in 139 children and adolescents with and without autism spectrum disorder aged 8–17 years. There were no significant differences between groups on parents’ education, first language spoken at home, intelligence quotient, and age. We examined the influence of diagnosis of autism spectrum disorder, age group, intelligence quotient, and attention problems on the self-report of personality validity indexes (indicators of overly negative or positive, random, inattentive or inconsistent responses). The findings suggest that participants with autism spectrum disorder were more likely to show at least one validity caution on their self-report of personality as compared to their matched typically developing peers. However, this difference might be a result of comorbid attention problems, rather than having a diagnosis of autism spectrum disorder. The diagnosis of autism spectrum disorder was not a significant predictor of the validity indexes. Participants, with and without autism spectrum disorder, with fewer attention problem ratings, higher intelligence quotient scores, and adolescents compared to children showed better validity outcomes. Lay abstract Using self-report questionnaires is an important method in the assessment and treatment of children with autism. Self-reports can provide unique information about children’s feelings and thoughts that is not available through other methods such as parent-reports. However, many clinicians are not sure whether children with autism can provide accurate self-reports. To study this, we examined 139 children and youth with and without autism aged 8–17 years. We looked at the effect of having autism, as well as other factors such as age, intelligence quotient, and attention problems on the validity of self-reports in these children. We examined if the children gave overly negative or positive answers and if they responded to the questions randomly or without paying attention. We found that children with autism can provide acceptable self-reports. However, they have more validity problems compared to their peers without autism. Our findings showed that this difference might be related to having attention problems in addition to autism, rather than having autism by itself. Children, with and without autism spectrum disorder, with fewer attention problems and higher intelligence quotient scores and those in the older age group, showed better validity. This article suggests that clinicians can use self-report measures for children with autism, but they should pay attention to important factors such as children’s intelligence quotient and attention problems.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».