Biological, Family and Cultural Predictors of Personality Structure analysis based on personality prediction models constructed by open data source

TL;DR

This study investigates how biological, family, and cultural factors influence personality traits during the COVID-19 pandemic using regression and decision tree models on open data.

Contribution

It identifies various demographic and cultural predictors of personality traits, highlighting the multifaceted influences on personality structure.

Findings

Females differ from males in personality traits.

Personality changes with age and upbringing.

Urban upbringing correlates with higher agreeableness and openness.

Abstract

Objective: This study takes further step on understanding personality structure in order to cope with the mental health during the COVID-19 global pandemic situation. Methods: Categorized the independent variables into biological, family and cultural predictors according to the datasets of the Big-5 personality survey online. And established multiple regression prediction models and exhaustive CHAID decision tree model of each personality trait. Results: Females are different from males in personality. The personality changes when growing. One-handed dominants are less agreeable and open than those who use both hands. Different sexual orientation does have variety personality. Native language used and education attainment is significantly related to personality accordingly. Marriage did help shaping personality to be more extroverted, less neurotic or agreeable and more conscientious and open. People raised in urban are more agreeable and open. Neurotic and open people often come from small families. person participated in voting are more extroverted, conscientious and open but less neurotic and agreeable. Different religions and races have different characteristics in each dimension of personality and there is no clear pattern have been found. Conclusion: Personality traits are indeed affected by multiple confounding factors. but the exploration on multiple cultures predictors still needed more details