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Information Journal Paper

Title

Determine the structure of relationships between family members based on personality factors (network data analysis method)

Pages

  189-208

Abstract

 The purpose of this study Network data analysis application in family distance according to personality dimensions (NEO). Network analysis is a powerful method for analyzing communications between network members, which is a personality dimension and a graphical representation of matrix data. This research is a correlation study. In this research, which used cluster random sampling, 130 families with girls aged 11 to 20 from districts 1 and 3 of Tehran were chosen. The NEO Personality traits Inventory was used for data collection. The communication network depicted based on the data using network analysis technique and Fraterman-Reingold algorithm with qgraph and bootnet in software R. This pattern represented that between the subscales of personality, extraversion of the highest centrality, neuroticism had the highest negative connection and openness to experience had the least connection with other subscales. Conscientiousness-agreeableness had the most positive relationship and neuroticism-extraversion had the most negative relationship with each other.

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    APA: Copy

    Zamani, Fahimeh Sadeghi, & ZARGHAMI, MOHAMMAD HOSSIEN. (2018). Determine the structure of relationships between family members based on personality factors (network data analysis method). TRAINING MEASUREMENT, 9(33 ), 189-208. SID. https://sid.ir/paper/214694/en

    Vancouver: Copy

    Zamani Fahimeh Sadeghi, ZARGHAMI MOHAMMAD HOSSIEN. Determine the structure of relationships between family members based on personality factors (network data analysis method). TRAINING MEASUREMENT[Internet]. 2018;9(33 ):189-208. Available from: https://sid.ir/paper/214694/en

    IEEE: Copy

    Fahimeh Sadeghi Zamani, and MOHAMMAD HOSSIEN ZARGHAMI, “Determine the structure of relationships between family members based on personality factors (network data analysis method),” TRAINING MEASUREMENT, vol. 9, no. 33 , pp. 189–208, 2018, [Online]. Available: https://sid.ir/paper/214694/en

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