Introduction: 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.

Purpose: Therefore, some difficulty may often raise in finding associations between these variables using regression models as regression models are built on restrictive assumptions.

Methods: 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.

Results: The appropriate neural network model for EI prediction is a hyperbolic tangent transfer function with two neaurons in the hidden layer and a sigmoid transfer function in the output layer. This network was able to predict EI in most of its dimentions with significant correlations and could demonstrate the neural network’s advantage over regression models in predicting EI using sociological variables.

Conclusion: 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.

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Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.