文章基本信息

标题：Application of trait anger and anger expression styles scale new modelling on university students from various social and cultural environments
本地全文：下载
作者：Fethi Arslan
期刊名称：Educational Research and Reviews
印刷版ISSN：1990-3839
出版年度：2016
卷号：11
期号：6
页码：288-298
DOI：10.5897/ERR2015.2563
语种：English
出版社：Academic Journals
摘要：The purpose of this study is to investigate the differences in anger traits of university students and teacher candidates studying in various social and cultural regions, of Batman and Denizli, Turkey. Modelling anger and anger expression style scale according to some variables such as age, gender, education level, number of siblings, parents’ education level was used. The study was carried out in the academic year of 2014-2015. The study population consists of a total of 551 students: 263 are from Batman University; of these, 166 are females and 97 are males; 114 are between the ages of 16-20, and 121 are between the ages of 21 and 24, and 28 are over 25. 288 are from Pamukkale University; of this, 169 are females and 119 are males; 134 are between the ages of 16-20, and 135 are between the ages of 21 and 24, and 19 are over 25. Demographic characteristics and trait anger-anger expression style scale was employed as measurement tools in data collection. In order to test whether the structure of Trait Anger and Anger Expression Styles Scale (TAAESS), defined as 4 factors, was verified in predetermined participation group, Confirmatory Factor Analysis (CFA) (Garson, 2005) was conducted using LISREL program (Joneskog and Sorbom, 1998) and its Turkish version that was translated by Ozer (1994). After TAAESS validity and reliability study was carried out, first level 4 factor Robust ML model was developed in reliability study. Result of this study showed that 4 factors of scale can bring out more consistent results even with 30 items as well. Differences in anger subscales including trait anger, anger-in, anger-out, anger control level differences were found statistically meaningful. It can be proposed that the origin of these differences is probably from the participants’ different social and cultural environments. Among suggestions given for professionalization of university students, and future educators against anger, it is proposed that model of 4 factor Robust ML with 30 items should be employed in researches by increasing social and cultural activities, raising awareness of society against anger and utilizing TAAESS.
关键词：Anger behaviours; education; Robust ML Model