Allahyari Elahe
Social Determinants of Health Research Center, Faculty of Health, Department of Epidemiology and Biostatistics, Birjand University of Medical Sciences, Birjand, Iran.
Biomedicine (Taipei). 2020 Sep 1;10(3):18-24. doi: 10.37796/2211-8039.1029. eCollection 2020.
Emotional intelligence (EI) constitutes a whole set of non-cognitive capabilities, competencies, and skills that affect one's ability to deal successfully with environmental demands and pressures. Different factors such as gender, age, education, place of residence, etc. can influence this variable. Nevertheless, the influence of a multitude of factors involved in behavioral phenomena cannot often be controlled.
Therefore, some difficulty may often raise in finding associations between these variables using regression models as regression models are built on restrictive assumptions.
In these cases, models such as artificial neural networks are excellent alternatives to regression models. In this study, the neural network model was used in SPSS software to predict the pattern held among the variables of age, gender, occupation, marital status, and education for predicting the EI of 901 individuals aged from 17 to 73 years.
The appropriate neural network model for EI prediction is a hyperbolic tangent transfer function with two neurons in the hidden layer and a sigmoid transfer function in the output layer. This network was able to predict EI in most of its dimensions with significant correlations and could demonstrate the neural network's advantage over regression models in predicting EI using sociological variables.
This model is able to estimate the EI level in different occupational, educational, gender, and age groups, and provide the ground for planning to address potential deficiencies in each group.
情商（EI）构成了一整套非认知能力、胜任力和技能，这些能力会影响一个人成功应对环境需求和压力的能力。不同因素，如性别、年龄、教育程度、居住地点等，都会影响这一变量。然而，行为现象中涉及的众多因素的影响往往难以控制。
因此，使用回归模型来寻找这些变量之间的关联时，常常会遇到一些困难，因为回归模型是基于限制性假设构建的。
在这些情况下，诸如人工神经网络之类的模型是回归模型的绝佳替代方案。在本研究中，在SPSS软件中使用神经网络模型来预测年龄、性别、职业、婚姻状况和教育程度等变量之间的模式，以预测901名年龄在17至73岁之间个体的情商。
用于情商预测的合适神经网络模型是一个双曲正切传递函数，隐藏层有两个神经元，输出层有一个Sigmoid传递函数。该网络能够在其大多数维度上预测情商，具有显著相关性，并且能够证明在使用社会学变量预测情商方面，神经网络比回归模型更具优势。
该模型能够估计不同职业、教育、性别和年龄组的情商水平，并为规划解决每组潜在缺陷提供依据。
