Investigation of the relationship between the perfectionist traits and the relationship satisfaction in dyadic relations of the university students

The main purpose of the current research was to investigate the relationship between relationship satisfaction and demographic variables, perfectionist personality traits in dyadic relationships. The participants of the study were 799 students, who are studying at of Anadolu University. The instruments of the study were Dyadic Almost Perfect Scale, Relationship Assessment Scale, Personal Information Form developed by the researcher was used to collect the data of the study. In order to analyze the data gathered from 799 participants Independent-samples t-test, sequential hierarchical regression anlaysis were employed, and descriptive statistics and Pearson correlation coefficients were computed. As a result of the analysis, it was observed that the relationship satisfaction and order level of university students were high; discrepancy and high standards level were medium level. Group comparisons showed that a relationship satisfaction scores are not differ regarding gender. There was a positive relationship between university students' relationship satisfaction and relationship time and order sub-dimensions. There was a negative relationship relationship satisfaction, the discrepancy and high standards sub-dimensions, but no significant relationship between the ages. For female students, ıt was observed that as the relationship duration and order increases, the relationship satisfaction increases. But, as the discrepancy increases, the relationship satisfaction decreases. For male students, it is observed that as the discrepancy increases, relationship satisfaction decreases and as the high standards increases, the relationship satisfaction increases too. As a result of the hierarchical regression analysis; the duration of the relationship, high standards, discrepancy and order sub-dimensions are a significant predictor of the model but gender and age are not significant predictor of model.