(PS10-C66) Predicting Adolescent Resilience Through Measures of Emotion Dysregulation

Background: Resilience (R) is the dynamic ability to overcome adversity (Rutter, 2006). One factor that forms R is emotion regulation (ER), which refers to the processes that facilitate appropriate emotional reactions aligned with an individual’s goals (Thompson, 1994). In youth, difficulties with ER, or emotion dysregulation (ED), are associated with maladaptive psychological and socioemotional outcomes (McLaughlin et al., 2011). Previous research has found that adolescents who are able to better regulate their emotions have more R (Mestre et al., 2017), which is associated with more adaptive socioemotional outcomes. In the current study, we assessed whether a newer ED measure accounted for any additional variance beyond an established ED measure when predicting R.   

Method: Online survey data were collected via Qualtrics from 109 adolescents (73% female; M = 17.7 years old; 63% white) currently enrolled in an introductory college course. Data collection for this study is ongoing. The personal R subscale of the Child Youth Resilience Measure (CYRM-R; Jefferies et al., 2019), the established Difficulties in Emotion Regulation Scale (DERS; Gratz & Roemer, 2004), and the newly created Reactivity, Intensity, Polarity and Stability Questionnaire–Youth (RIPoSt–Y; Sesso et al., 2021) were used. The six DERS subscales of nonacceptance of emotional responses (NER), difficulties engaging in goal directed behaviors (DB), impulsive control difficulties (ICD), lack of emotional awareness (LEA), limited access to emotion regulation strategies (LER), and lack of emotional clarity (LEC) were assessed. The three RIPoSt–Y subscales of interpersonal sensitivity (IS), emotional reactivity (RER), and affective instability (AI) were assessed. 

Results: First, two multiple linear regressions were conducted. In the first regression, the DERS accounted for a significant proportion of variance in the CYRM (F(6,102) = 5.58, p < .001, R^2 = .25); in the second regression the RIPoST-Y also accounted for a significant proportion of variance in the CYRM (F(3,1055) = 9.27, p < .001, R^2 = .21). Correlations were then examined, and variables were entered into a hierarchical regression equation in order of the magnitude of the correlation. In this hierarchical regression, the following variables were significant predictors of R: DERS LER (F(1,107) = 21.69, p <.001, R^2 = .17); RIPoST-Y AI (F(1,106) = 55.3939, p = .022, R^2 = .212, R^2 = .21); RIPoST-Y IS (F(1,100) = 44.8080, p = .031, R^2 = .271, R^2 = .27); and DERS DB (F(1,99) = 4.37, p = .039, R^2 = .30). When all variables were entered simultaneously in a multiple linear regression, the following predictors were found to be significant: DERS LER (p = .037), DERS DB (p = .039), and RIPoST-Y IS (p = .024). 

Conclusion: The findings first suggested that the RIPoST-Y accounted for R above and beyond scores on the DERS. Further examination of the subscales of each instrument suggested that the LER and DB subscales on the DERS and the IS subscale on the RIPoST-Y predicted youth R on the CYRM. The IS subscale on the RIPoST-Y is a relevant subscale to use when studying R, as R is partially formed through interpersonal relationships. Future studies should consider using these specific subscales when looking at the relationship between ED and R.