Poursaeid Esfahani M, Sharifi H, Akrami N. (2021). Predicting high-risk behavior based on self-differentiation and interpersonal problems in female students.
Rooyesh.
10(3), 55-64.
URL:
http://frooyesh.ir/article-1-2529-en.html
1- PhD student in psychology, Department of Psychology, Faculty of Educational Sciences and Psychology, University of Isfahan, Isfahan, Iran.
2- M.A of family counseling, Department of counseling, Faculty of Educational Sciences and Psychology, University of Isfahan, Isfahan, Iran
3- Assistant Professor of Psychology, Department of psychology, Faculty of Educational Sciences and Psychology, University of Isfahan, Isfahan, Iran. , N.Akrami@edu.ui.ac.ir
Abstract: (1708 Views)
The purpose of this study is to predict High-risk behavior based on self-differentiation and interpersonal problems. The research population consisted of female students of Isfahan University. 100 students were selected by multistage cluster sampling. The research method is a descriptive correlation. The research tools include of Risk-Taking Scale (Zadehmohamady et al, 2011), Differentiation of Self Inventory-Short Form (Drake, 2011), and Inventory of Interpersonal Problems (Barkham et al, 1996). The Data analysis was performed using stepwise regression analysis. The results of the regression analysis showed that aggression can positively and significantly predict high-risk driving, violence, and alcohol consumption. Assertiveness and sociability are able to positively and significantly predict sexual relations. Openness can negatively and significantly predict using drugs and alcohol. Among the dimensions of self-differentiation, I-position is negatively and significantly able to predict violence and the emotional cut-off is negatively and significantly able to predict cross-sex friendship. Based on the results, it can be concluded that self-differentiation and interpersonal problems should be considered for planning to reduce or prevention of high-risk behaviors.
Type of Article:
Research |
Subject:
Social psychology Received: 2020/12/25 | Accepted: 2021/03/30 | ePublished: 2021/05/31