Examining the Relation between Social Values Perception and Moral Maturity Level of Folk Dancers
Notice bibliographique
Résumé
The purpose of the present study is to examine the relation between social values perceptions and moral maturity levels of folk dancers, and evaluate this relation in terms of some variables. The relational screening model was used in the study. The “Multi-dimensional Social Values Scale”, which was developed by Bolat (2013), and the “Moral Maturity Scale”, which was developed by Şengün and Kaya (2007), were used as the data collection tools. The sampling of the study consisted of 251 volunteering individuals who were selected with the Random Selection Technique from among the individuals who participated in Interuniversity Folk Dances Championship in 2016-2017. In the scope of the study, the descriptive statistics were computed for social values perceptions and moral maturity levels of the folk dancers. In evaluating the data of the study, frequencies and percentage distributions were used as statistical method. In addition, the t-test was used for paired groups, the one-way variance analysis (ANOVA) was used for multiple groups, and the Pearson Correlation Analysis was used to detect the relation between the variables. The p value of significance was taken as 0,05 in different tests.When the findings of the study were examined, it was determined that the social value perceptions and moral maturity levels of the folk dancers who participated in Interuniversity Folk Dances Championship were at a high level, and there was a significant relation in positive direction between the perceptions in all sub-dimensions of the Multi-Dimensional Social Values Scale and the moral maturity levels of the participants. In this context, it is possible to claim that as the moral maturity levels of the folk dancers increase, so do their social values perceptions. When the social value perceptions were analyzed in terms of sub-dimensions, it was determined that the highest perception was in religious values sub-dimension, and the lowest perception was detected in political values. When the findings of the study were analyzed in terms of demographical variables, it was determined that there were significant differences between some of the sub-dimensions of the Social Values Scale and the “gender, the faculty studied at, folk dances experience, income level and the place where the participant spend most of his/her life” variables.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».