Moving beyond Ordinary Factor Analysis in Studies of Personality and Personality Disorder: A Computational Modeling Perspective
Abstract
Almost all forms of psychopathology, including personality disorders, are arrived at through complex interactions among neurobiological vulnerabilities and environmental risk factors across development. Yet despite increasing recognition of etiological complexity, psychopathology research is still dominated by searches for large main effects causes. This derives in part from reliance on traditional inferential methods, including ordinary factor analysis, regression, ANCOVA, and other techniques that use statistical partialing to isolate unique effects. In principle, some of these methods can accommodate etiological complexity, yet as typically applied they are insensitive to interactive functional dependencies (modulating effects) among etiological influences. Here, we use our developmental model of antisocial and borderline traits to illustrate challenges faced when modeling complex etiological mechanisms of psychopathology. We then consider how computational models, which are rarely used in the personality disorders literature, remedy some of these challenges when combined with hierarchical Bayesian analysis.
Nathaniel Haines
Data Scientist & Computational Psychologist, PhD
An academic Bayesian who is currently exploring the high dimensional posterior distribution of life