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Introducing Copula as a Novel Statistical Method in Psychological Analysis.

Elham Dehghani1, Somayeh Hadad Ranjbar2, Moharram Atashafrooz3

  • 1Department of Psychology, Rafsanjan Branch, Islamic Azad University, Rafsanjan 7718184483, Iran.

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|August 7, 2021
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Summary
This summary is machine-generated.

This study reveals that poor self-esteem strongly correlates with severe anxiety (over 98% probability). Copula functions effectively model the dependence structure between psychological parameters like self-esteem and anxiety.

Keywords:
anxietycopuladependence structuremathematical modelingprobability matrixprobability theorypsychologyself-esteemsocial science datastatistics

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Area of Science:

  • Psychology
  • Statistics

Background:

  • The relationship between psychological parameters is well-studied.
  • The dependency structure of correlated psychological parameters remains under-investigated.

Purpose of the Study:

  • To introduce a novel approach using copula functions for analyzing the dependence structure of psychological parameters.
  • To establish the dependence structure between self-esteem and anxiety in university students.

Main Methods:

  • Utilized the Coopersmith Self-esteem Inventory and Zang Anxiety Scale for data collection from 141 Iranian university students.
  • Applied copula functions to model the joint distribution of self-esteem and anxiety.
  • Identified the Frank copula as the best fit for the data.

Main Results:

  • Developed a probability matrix illustrating the relationship between self-esteem and anxiety.
  • Found that poor self-esteem is associated with severe or very severe anxiety (probability > 98%).
  • Determined that strong self-esteem is linked to normal and mild anxiety (probability ≈ 80%).

Conclusions:

  • Copula functions offer a promising method for analyzing the dependence structure of psychological parameters.
  • The findings highlight a significant probabilistic link between self-esteem levels and anxiety severity.