Idiographic Dynamics: Measurement & Analysis at the Individual Level
Whereas the standard approaches to assessing psychological constructs are often meant to reflect time-varying processes and phenomena (such as learning, self-regulation, etc.), they rely heavily (if not exclusively) on cross-sectional methods. In the case of psychopathology, for instance, the diagnosis of major depressive disorder requires presenting symptoms over a 2-week period, while generalized anxiety disorder requires the presence of worry and accompanying symptoms over 6 months. However, we typically do not assess depressed patients over 2 weeks or anxious patients over 6 months—favoring structured clinical interviews that ask patients to rate the severity of their experience over specified periods retrospectively and to reflect on dynamic processes such as worry, rumination, emotion regulation, and avoidance behavior.
Multivariate time series data – the intensive repeated measurement of several variables within individuals – facilitates the exploration of idiographic dynamics. That is, we can look at the way constructs manifest within a single individual in time. Moreover, we can examine the dynamic interplay between constructs in time, within a single individual. This talk will present several methods for collecting, analyzing, and interpreting idiographic data. Specifically, I will focus on factor analytic and network modeling approaches. Network models provide tools for digitizing complex sets of relationships in a relatively parsimonious way – including ways to model and understand comorbidity. However, much of the work in this area has been limited to nomothetic modeling of cross-sectional data. The present talk will discuss network models generally, the application of network modeling to multivariate time series data, and how to use centrality measures – the metrics associated with network models – to understand the ways in which symptoms and behaviors are structurally connected to and (possibly) causally influential over other symptoms and behaviors. In addition, I will demonstrate how person-specific factor analytic techniques, referred to as P-Technique (Cattell et al. 1947), can be used to distill symptom variation psychological disorders to a small number of clinically-relevant dimensions. Finally, I will discuss how these techniques can be leveraged to provide granular assessment data to inform precision treatment approaches.
Aaron Fisher graduated from Penn State University with a degree in clinical psychology in 2012. After a year as a postdoctoral scholar at Stanford University, he joined the Clinical Science faculty in the Department of Psychology at UC Berkeley in 2013. His current research interests lie in the application of person-specific methodologies to the investigation of psychopathology, psychotherapy, and psychophysiology. Aaron’s overarching goal is to leverage intensive repeated measures data to model individuals as dynamic systems. To this end, his lab has worked to quantify individuals on multiple levels, including physiological, emotional, and behavioral. The long-term goal of Aaron’s research program is to use these models to better understand and predict outcomes in physiological and psychological health. This works seeks to facilitate the formulation and implementation of precision medicine methodologies in behavioral science research and applied interventions.
While Aaron’s principal goal is to understand the underlying causal dynamics in psychopathology, it has become apparent to him that the articulation of a person-specific model of psychological research is an equally important goal. That is, while he hopes to utilize the greater granularity of person-specific methods to draw conclusions not possible with aggregated approaches, the process of doing this work has demonstrated just how heterogeneous these processes are and how important it is to articulate and disseminate the staggering diversity of individuals – in the organization of their thoughts, feelings, and actions, and in the way they respond to therapy.