Forecasts based on the quality of life of early maladaptive personality traits and layout of married students | Abstract

International Journal of Medical Research & Health Sciences (IJMRHS)
ISSN: 2319-5886 Indexed in: ESCI (Thomson Reuters)


Forecasts based on the quality of life of early maladaptive personality traits and layout of married students

Author(s):Bita Yoosefi, Afshin Tayyebi* and Mehdi Shah Nazari

The purpose of this research is quality of life based on the projected plan early maladaptive personality traits and is married students. The research is correlational study based on regression designs. The study population consisted of all 1394 samples married university students the freedom of worship and variable sampling scheme is 15 components as well as variable character trait has 5 components. So in total we have 20 components and according to Kramer method that can be calculated for each variable 20 to 40, by calculating 20 per variable, with the estimated total of 400 people were chose). Sampling was used and the sample of 50 questionnaires that distorted the analysis of 350 questionnaires were included. The results of the regression coefficients table shows the impact factors of duty, social alienation, unrelenting standards, environmental health, is statistically significant in predicting the criterion variable. But this shit impact factors neuroticism, extraversion, emotional inhibition, adaptation, and incompatible schemes other than social alienation and the criteria ineffective in predicting environmental health. The components of social relations based on neurosis, conscientiousness and compromise your schema evolution is not predictable failures and defects. According to the mental health component of neuroticism, conscientiousness and self-transformation scheme is not in itself sufficient disciplinary predictable. The impact factors of neurosis, compatibility and scheme incompatible with the exception of (Schema emotional inhibition, and impaired social aliens failure) was ineffective in predicting mental health.

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