A Cross-Cultural Comparative Study on Gender and Age-Cohort Differences in Multidimensional Social-Emotional Competence
Notice bibliographique
Résumé
Background and Purpose: Gender is one of the most commonly studied predictors of social-emotional competence (SEC). Children and adolescents develop their SEC over time under the influence of gender-based cultural practices. While previous literature observed an overall gender difference in SEC, typically favoring girls, there are limitations to note. First, most studies have been conducted within a single cultural context, predominantly in Western societies, lacking a cross-cultural perspective. Second, previous research has largely focused on early childhood, with limited evidence available on gender differences in SEC varying throughout later developmental stages. Third, most studies have measured SEC as a composite variable, resulting in less nuanced findings on specific SEC domains. Also, little work has been done to confirm measurement invariance of SEC assessments before cross-group comparisons. To fill these gaps, this study examines how gender differences in SEC vary between North American (NA) and East Asian (EA) contexts, across middle childhood and adolescence, using a comparable measure of multidimensional SEC. Methods: Using the first wave of the OECD Survey of Social and Emotional Skills, this study analyzed student self-report data (N=25,206, 49.9% girls) collected from two age-cohort samples (52.5% age 10 and 47.5% age 15) from two EA countries–China (28.7%) and South Korea (25.1%)–and two NA countries–Canada (20.1%) and the USA (25.3%). This study used indicators of six SEC dimensions, previously studied to be equally relevant to both cultural samples: Emotional Control (6-item), Optimism (6-item), Open-mindedness (9-item), Task Performance (14-item), Prosociality (10-item), and Leadership (6-item). We first applied multidimensional polytomous item response theory modeling to detect any differential item functioning (DIF), then conducted linear regression analysis to examine the effects of gender and its interactions with culture and age-cohort on all six SEC dimensions. Results: Multigroup DIF analysis suggested that most items showed a good fit and no systematic differences across gender, cultural, and age-cohort subgroups, except for three items that were excluded from further analysis. Regarding the main effects of gender, boys had higher levels of Emotional Control, Optimism, and Open-mindedness, while girls had higher scores on Prosociality and Task Performance (all differences p<.001). Across cultural and age-cohort subgroups, gender differences in SEC were most pronounced among the 15-year-old EA sample: EA adolescent girls had significantly lower levels of Emotional Control, Open-mindedness, and Optimism than EA adolescent boys. These gender gaps were larger than those observed within younger EA children, NA children, and NA adolescents. Moreover, EA adolescent girls tended to have lower levels of Prosociality and Task Performance than EA adolescent boys (both differences p<.05), unlike other subgroups that favored girls. Conclusions and Implications: To our knowledge, this study is one of the first studies that examined how gender differences in multidimensional SEC manifested across different age-cohorts and cultural contexts, using the measurement tool found to be invariant across compared subgroups. This study calls for more research to further explain the observed phenomenon, while providing implications for practice to support healthy development of East Asian girls.
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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 | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».