Cluster Analysis of Personality Types Using Respondents’ Big Five Personality Traits

Abstract

This study utilized a mixed model approach, incorporating k-means clustering analysis for data examination, discriminant analysis for classification, and multilayer perceptron neural network analysis for prediction. After removing inadequate samples and outliers, the total number of observations was 19,692 for this study, which was collected through an interactive online personality test (i.e., Big Five Personality Traits) in 2012. The empirical results based on the k-means clustering analysis identified four different personality clusters using the total score of Big Five Personality Traits (Extraversion, Neuroticism, Agreeableness, Conscientiousness, and Openness to Experience). The empirical results obtained from the k -means clustering analysis revealed the presence of four distinct personal clusters, determined by the total scores of the Big Five Personality Traits. The accuracy of the clustering analysis was further tested using discriminant analysis, which indicated significant difference among the cluster means and correctly classified 95.5% of the original grouped cases. For predictive modeling, a multilayer perceptron neural network framework was used. The network had a 5-6-4 structure and was employed to determine the personality classification of participants. Notably, the model achieved 99.4% accuracy in correctly classifying the training grouped cases and 99.2% accuracy for the testing grouped cases. The results of this study offer valuable insights into understanding the personalities of participants, with implications for various domains such as psychology, social sciences, cultural studies, and economics.