Equilibria in Personality States: A Conceptual Primer for Dynamical Analyses

We provide a basic, step-by-step introduction to the core concepts and mathematical fundamentals of dynamic systems modeling through applying the Change as Outcome model, a simple dynamical systems model, to personality state data. This model characterizes changes in personality states with respect to equilibrium points, estimating attractors and their strength in time series data. Using data from the Personality and Interpersonal Roles (PAIRS) study, we find that mean state is highly correlated with attractor position but weakly correlated with attractor strength, suggesting strength provides added information not captured by summaries of the distribution. We then discuss how taking a dynamic systems approach to personality states also entails a theoretical shift. Instead of emphasizing partitioning trait and state variance, dynamic systems analyses of personality states emphasize characterizing patterns generated by mutual, ongoing interactions. Change as outcome modeling also allows for the effects of personality development after significant life changes to be conceptualized in more nuanced ways, separating effects on characteristic states after the significant change and how people are drawn towards those states (an aspect of resiliency). Estimating this model demonstrates core dynamics principles and provides quantitative grounding for measures of “repulsive” personality states and “ambivert” personality structures. Supplementary materials: https://osf.io/dps4w.