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Sensitivity checks /
Sensitivity check adjusting for anhedonia status
- Charlotte Vrijen
- Catharina A. Hartman
- Peter de Jonge
- Eeske van Roekel
- Albertine Oldehinkel
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Description: The instrength and outstrength plot shows the individual variation in instrength and outstrength in four subgroups: anhedonic participants with a high happy bias (n = 8), anhedonic participants with a low happy bias (n = 13), control participants with a high happy bias (n = 17) and control participants with a low happy bias (n = 10). JOY = feeling joyful; POS = pleasant experiences; INT = feeling interested in things around me; SAD = feeling sad; IRR = feeling irritated; WOR = worrying; NEG = unpleasant experiences. To assess the plausibility that anhedonia status may drive the results we found for happy bias we first tested whether the general affect dynamics we found to be associated with high versus low happy bias were also associated with anhedonia status. Permutation test 1, which was originally used to test whether the reward-related positive affect nodes JOY and POS more strongly predicted moment-to-moment affect dynamics in the high happy bias group than in the low happy bias group, was repeated to explore whether a similar difference in affect dynamics could also be found between a control and an anhedonia group. As this was the case it is therefore important to adjust for anhedonia status. To be able to adjust for anhedonia status, the subject-specific affect network paths (i.e., the random effects) based on the original VAR models were used to calculate the subject-specific total strength of the outgoing edges from JOY and POS. This resulted in a separate value for each participant of how strongly JOY and POS together predicted the other nodes in the network and themselves at the next moment. Subsequently, we ran a linear regression analysis in SPSS version 25 with the total strength of all outgoing edges of JOY and POS per participant as outcome variable and dummy variables of anhedonia status and high versus low happy bias as predictors.