Revisiting the IPIP-NEO personality hierarchy with taxonomic graph analysis

Abstract

Describing and understanding personality structure is fundamental to predict and explain human behavior. Recent research calls for large personality item pools to be analyzed from the bottom-up, as item-level analysis may reveal meaningful differences often obscured by aggregation. This study introduces and applies Taxonomic Graph Analysis (TGA), a comprehensive network psychometrics framework aimed at identifying hierarchical structures from the bottom-up, to an open-source 300-item IPIP-NEO dataset ( N = 149,337). This framework addresses key methodological challenges that have hindered accurate recovery of hierarchical structures, including local independence violations, wording effects, dimensionality assessment, and structural robustness. TGA revealed a three-level structure composed of 28 first-level dimensions (facets), 6 second-level dimensions (traits), and 3 third-level dimensions (meta-traits). Although some dimensions aligned with the theoretical IPIP-NEO structure, there were considerable deviations including the emergence of Sociability, Integrity, and Impulsivity traits at the second-level and a novel Disinhibition meta-trait at the third-level. The overarching theme of our findings was a hierarchical structure that integrated empirical and theoretical findings that have been scattered across the personality literature, demonstrating TGA’s value to investigate hierarchical psychological constructs. This study contributes to discussions on personality taxonomy by providing a rigorous, data-driven perspective on the IPIP-NEO’s hierarchical structure. Describing personality structure is important to understand why people do what they do. Conventional approaches often rely on analyzing pre-established structures (e.g., facets), taking them at face value, to analyze up to and beyond the Big Few. These approaches potentially overlook how alternative groupings might form based on the relations between the items. When analyzing item-level relations, there are many methodological obstacles that must be overcome; otherwise, the accurate recovery of these item groupings can be obscured. This study introduces Taxonomic Graph Analysis (TGA), a network psychometrics framework that can build personality structures from the bottom-up. TGA addresses previous methodological challenges such as local independence violations (redundant or semantically similar items), wording effects (how item polarity affects responses), dimensionality assessment, and structural robustness. This framework was applied to the 300-item IPIP-NEO personality inventory ( N = 149,337) where we uncovered a three-tiered personality structure composed of 28 facets, 6 traits, and 3 meta-traits but no general factor. Importantly, although there was alignment with the theoretical IPIP-NEO structure, there were considerable differences, like a novel Disinhibition meta-trait and the emergence of the Sociability, Integrity, and Impulsivity trait domains. These results integrate scattered empirical and theoretical findings across the personality literature, providing a more cohesive understanding of how the IPIP-NEO hierarchy is structured. Taken together, these results demonstrate TGA’s value for investigating complex psychological constructs and contribute to ongoing discussions about how personality can be defined and measured, with implications for both theory and practice.