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Research Methods and Statistics

Examining person-level networks of emotion regulation difficulties

(PS10-81) Examining Person-level Networks of Emotion Regulation Difficulties

Saturday, November 16, 2024
1:00 PM – 2:00 PM EST
Location: Franklin Hall, Level 4, Marriott Downtown
Abstract Text: People are unique, intricate, and continually changing, despite the assumption of uniformity made by many researchers through their use of nomothetic (group-based) statistical methods (Molenaar, 2004). Similarly, literature on emotion regulation difficulties has generally assumed homogeneity across individuals, at the expense of dynamic experiences and between-person differences. The advent of idiographic methods allows for person-to-person variability to be delineated across time (Beltz & Gates, 2017). The current study used an idiographic statistical approach, more specifically, group iterative multiple model estimation (GIMME), to examine time series data among person-level networks. GIMME estimates a network with contemporaneous and lagged directed relations among variables for each person. The current sample (N = 4; data collection is in progress with a target N of 40) collected includes participants who scored ≥ 89 on the Difficulty with Emotion Regulation Scale (DERS), indicative of having difficulties regulating their emotions. Participants were administered surveys four times/day over 30 days to assess for their momentary emotion dysregulation, psychological distress, life satisfaction, and burnout. Preliminary analyses demonstrated an average alternative fit indices of RMSEA = .07, SRMR = .11, CFI = .91, and NNFI = .86. The models revealed high levels of heterogeneity in emotion regulation difficulties among participants, with minimal features shared among participants. As an example, for one participant, emotion regulation difficulties were closely related to burnout and life satisfaction. Whereas for another participant, only difficulty getting work done predicted burnout. By collecting data that considers temporal dynamics as a unique system, these networks may inform clinicians on the relationships that promote and maintain psychopathology for each client. Moreover, future clinicians may be better able to personalize treatment and target specific factors maintaining psychological problems.