Summary: For 40 years, the Big Five personality model has dominated psychology, but new research suggests it may be incomplete. Using taxonomic graph analysis, researchers mapped personality from the ground up, uncovering new meta-traits and traits not captured by the Big Five.
The study’s bottom-up method highlights complex relationships between personality items, leading to a richer, more precise hierarchy. Beyond personality, this approach could also transform how clinicians classify and diagnose mental health disorders.
Key Facts:
- Big Five Expansion: The new model identified six traits, including sociability, integrity, and impulsivity, expanding beyond the traditional five.
- Bottom-Up Approach: TGA builds personality structures by analyzing statistical relationships between survey items.
- Psychopathology Impact: This method could lead to rethinking diagnoses, such as reframing anxiety as a subtype of depression.
Source: Vanderbilt University
Alexander Christensen’s recent study probably won’t rewrite 40 years of history in the field of psychology, but he hopes that his research team’s quantitative approach to developing and evaluating personality structures triggers a discussion about how personality is defined and measured.
Such discussion could hold broader implications for the field of personality psychology and potentially for classifications in psychopathology.
Christensen and his colleagues’ article, “Revisiting the IPIP-NEO personality hierarchy with taxonomic graph analysis,” was published in June in the European Journal of Personality.
Personality surveys, informed by theoretical personality structures, are often organized in a hierarchy, and offer people a scientific understanding of who they are.
For 40 years, the Big Five personality structure has been a highly regarded tool for creating psychological profiles. The Big Five traits are conscientiousness, agreeableness, neuroticism, openness to experience, and extraversion.
Christensen, an assistant professor of psychology and human development at Vanderbilt Peabody College of education and human development, says that the Big Five structure has been a culturally robust and predictive model for personality, but he believes it can be improved using advanced data science methods.
Common methods for understanding and measuring personality begin with the Big Five traits and connect to more refined characteristics further down in the hierarchy in a top-down, siloed approach (as shown in the structure below).
The approach can miss important statistical relationships between items (or individual survey questions). Items are the building blocks of personality taxonomies. Understanding the relationships between them is key to developing scientifically valid structures higher up the hierarchy.
A new and improved approach to personality structures
In their new study, Christensen and his colleagues apply taxonomic graph analysis (TGA), a network for measuring the relationships between psychological variables, to the IPIP-NEO personality inventory.
Using TGA as the tool for building the personality structure allows relationships to emerge from the bottom-up by making empirical connections between items to “facets” (narrow personality characteristics) to traits like the Big Five. The quantitative power of TGA uncovers a new, more precise structure (shown below) for measuring and describing personality.
In the research team’s new three-tiered IPIP-NEO taxonomic structure, they found three meta-traits (stability, plasticity, and the new disinhibition), six traits (neuroticism, conscientiousness, openness, and three new ones: sociability, integrity, and impulsivity), and 28 facets.
Application to psychopathology
“Beyond personality, I think this bottom-up approach could have an impact on how we develop classifications of psychopathology,” Christensen said.
Psychopathology structures (as exemplified above) follow a similar top-down approach to personality structures. As an example of how TGA could lead to reclassification and a change in diagnoses, Christensen points to the overlapping symptoms associated with internalizing disorders, specifically depression and anxiety. They often are diagnosed together but as two distinct diagnoses.
However, a TGA investigation of psychopathology structures might reveal two types of depression with anxiety being a component of one of these types, not a distinct diagnosis itself. If that were the case, a mental health practitioner could possibly diagnose someone with a specific type of depression, rather than with depression and anxiety.
Even so, Christensen says, “The paper’s lasting impact will be in the value of taxonomic graph analysis and the methods we developed for investigating complex psychological structures.”
The importance of “team-science”
Christensen credits the breakthroughs of this paper to the “team-science” approach that bridged longstanding theoretical scholarship and recent innovations in data science.
“Lead author Andrew Samo approached me a few years ago with this idea of examining the hierarchical structure of personality, and I thought we had developed some tools that we could apply to the evaluation.
“We wouldn’t have been able to do this study without bringing together Andrew’s substantive and theoretical team and my quantitative team. It’s a great example of what team science can accomplish,” Christensen said.
If you would like to take a shortened version of the survey Christensen and colleagues investigated, it is freely available.
Christensen teaches Behavioral Data Science in the Psychological Sciences Ph.D. program and Quantitative Methods M.Ed. program. He also teaches Fundamentals of Data Science for the Data Science undergraduate minor and Advanced Statistics for Data Scientists in the Data Science M.S. program.
About this personality and psychology research news
Author: Jenna Somers
Source: Vanderbilt University
Contact: Jenna Somers – Vanderbilt University
Image: The image is credited to Neuroscience News
Original Research: Open access.
“Revisiting the IPIP-NEO personality hierarchy with taxonomic graph analysis” by Alexander Christensen et al. European Journal of Personality
Abstract
Revisiting the IPIP-NEO personality hierarchy with taxonomic graph analysis
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.
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