Department of Psychology, Rafsanjan Branch, Islamic Azad University, Rafsanjan 7718184483, Iran.
Department of Theology and General Islamic Courses, Vali-e-Asr University of Rafsanjan, Rafsanjan 7718897111, Iran.
Int J Environ Res Public Health. 2021 Jul 28;18(15):7972. doi: 10.3390/ijerph18157972.
During the past decades, the relationship between various psychological parameters had been studied in detail. However, the dependency structure of correlated parameters was rarely investigated. Knowing the dependence structure helps in finding the probability matrix of the interaction between the parameters. In this research, a novel approach was introduced in psychological analysis using copula functions. For this purpose, the self-esteem and anxiety of 141 university students in Iran were extracted using the Coopersmith Self-esteem Inventory and the Zang Anxiety Scale. Then the dependence structure of self-esteem and anxiety were established using copula functions. The Frank copula achieved the best fit for the joint variables of self-esteem and anxiety. Finally, the probability matrix of different classes of anxiety, taking into account self-esteem classes, was extracted. The results indicated that poor self-esteem leads to severe or very severe anxiety, with more than 98% probability, while strong self-esteem may lead to normal and mild anxiety, with about 80% probability. It can be concluded that the method was promising, and that copula functions can open a window to the dependence structure analysis of psychological parameters.
在过去的几十年中，人们详细研究了各种心理参数之间的关系。然而，相关参数的依赖结构很少被研究。了解依赖结构有助于找到参数之间相互作用的概率矩阵。在这项研究中，使用 Copula 函数在心理分析中引入了一种新方法。为此，使用库珀史密斯自尊量表和臧氏焦虑量表从 141 名伊朗大学生中提取了自尊和焦虑。然后使用 Copula 函数建立了自尊和焦虑的依赖结构。Frank Copula 最适合自尊和焦虑的联合变量。最后，提取了考虑到自尊等级的不同焦虑等级的概率矩阵。结果表明，低自尊导致严重或非常严重的焦虑的概率超过 98%，而高自尊可能导致正常和轻度焦虑的概率约为 80%。可以得出结论，该方法很有前景，Copula 函数可以为心理参数的依赖结构分析开辟一扇窗户。
