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Investigation of Social Appearance Anxiety of Students of Faculty of Sport Sciences and Faculty of Education in Terms of Some Variables

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  • Tarik SEVINDI

Abstract

This study was carried out to examine the social appearance anxiety of the faculty of sports sciences and faculty of education in terms of some variables. In this context, the social appearance anxiety scale was applied to the students of the sports sciences who received sports education and the students of the education faculty who did not receive sports education. Independent samples t-test, one-way ANOVA, and LSD tests were used to analyze the data. As a result of the analysis, the social appearance anxiety scale score was found 34.43 in women and 35.11 in men. On the other hand, the social appearance anxiety scale score was found to be 32.89 in students of faculty of sport sciences and 36.68 in students of faculty of education. There was no significant difference in social appearance anxiety scale scores in terms of gender variable (p>0.05). A significant difference was observed in the social appearance anxiety scale scores in terms of sport education status variable (p<0.001). The social appearance anxiety scores of the students who were satisfied with their own body weight and height were found lower than those who were not satisfied, which was found statistically significant (p<0.001). The results of this research showed that the social appearance anxiety of students of the faculty of sport sciences who receive sports education is lower than those of the faculty of education students who do not receive sports education.

Suggested Citation

  • Tarik SEVINDI, 2020. "Investigation of Social Appearance Anxiety of Students of Faculty of Sport Sciences and Faculty of Education in Terms of Some Variables," Asian Journal of Education and Training, Asian Online Journal Publishing Group, vol. 6(3), pages 541-545.
    • Handle: RePEc:aoj:asjoet:v:6:y:2020:i:3:p:541-545:id:2079
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    File URL: http://asianonlinejournals.com/index.php/EDU/article/view/2079/1605
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    Cited by:

    1. Chaman Verma & Zoltán Illés & Deepak Kumar, 2022. "(SDGFI) Student’s Demographic and Geographic Feature Identification Using Machine Learning Techniques for Real-Time Automated Web Applications," Mathematics, MDPI, vol. 10(17), pages 1-21, August.
    2. Deepak Kumar & Chaman Verma & Pradeep Kumar Singh & Maria Simona Raboaca & Raluca-Andreea Felseghi & Kayhan Zrar Ghafoor, 2021. "Computational Statistics and Machine Learning Techniques for Effective Decision Making on Student’s Employment for Real-Time," Mathematics, MDPI, vol. 9(11), pages 1-29, May.

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