The Structural Model of Predicting Social Interest based on Interpersonal Relationships, and Satisfaction of the Relationship with the Mediating Role of Love in Women

Document Type : Original Article

Authors

1 Ph.D student, Consulting Department, Arak Branch, Islamic Azad University, Arak, Iran

2 Associate Professor, Department of Counseling, Islamic Azad University

3 Department of Psychology, Faculty of Humanities, Islamic Azad University of Arak, Arak, Iran.

4 Assistant Professor, Counseling Department, Faculty of Humanities, Khomeyn Branch, Islamic Azad University, Khomeyn, Iran

Abstract

This research was conducted with the aim of providing a structural model for predicting social interest based on interpersonal relationships and marital satisfaction with the mediating role of love of couples. The cross-sectional research method is correlational. The statistical population of the current study was made up of all the women who referred to Sarai Mahalat in Tehran in 2021-2022, and among them, 305 people were selected by multi-stage cluster sampling method. In this research, the tools of social interest (Crandall, 1975), interpersonal relationships (Hurwitz et al., 1988), relationship satisfaction (Burns and Cyrus, 1988) and love (Stenberg, 1989) were used, all of which had acceptable validity and reliability. In order to analyze the data, SPSS-V23 and Lisrel-V7.8 software were used. In order to respond to the research hypotheses, structural equation modeling was used. The findings of the research showed that the model has a good fit. The results showed that interpersonal relationships have a direct effect on social interest in couples. Marital satisfaction has a direct effect on social interest in couples. Interpersonal relationships have an indirect effect on social interest in couples with the mediating role of love. Marital satisfaction has an indirect effect on social interest in couples with the mediator role of love. Therefore, paying attention to the mentioned variables helps researchers and therapists in prevention and designing more suitable treatments.

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