| dc.description.abstract | In the past, psychologists have utilized clustering algorithms to understand how person ality structure changes over time, i.e. as people age. Through factor analysis of everyday language terms that people use to describe themselves, psychologists have developed several models attempting to best describe personality. By analyzing how the factors of these models cluster together for different age groups, we can understand how personality structure changes over time. Split into two projects, this paper utilizes various techniques from Topo logical Data Analysis (TDA) to analyze personality from a topological perspective. The first project of this paper stems from the research done by Costa and McCrae (1976) to see how personality factors from Cattell's 16 Personality Factor Questionnaire cluster together. In [1], Costa and McCrae claim that each of the three age groups (young, middle, and old) can be described with three clusters of personality traits. Using persistent homology, our analysis claims that the middle and old age group admit at most two clusters. The second project of this paper deals with surveys of the Big 5 Personality Inventory, provided by Dr. Sam Gosling of the University of Texas in Austin. The MAPPER algorithm is utilized to represent this data as a weighted graph connecting clusters of personality factors across various age groups. | |
