Severity and traits - key findings
TF1: Self-consciousness
- high self-consciousness is associated with lower worry (could be non-linear relationship)
- actually it seems that this is because state severity regresses our the difference and the remainder is negative (check
‼ ) - worry follows state severity only in early pandemics, not later
TF2: Discontent (opposite of "positive")
- generally higher Anxiety/Avoidance in high TF, but more dissociation from state severity
- discontent associated with lower probability estimates (look at more closely)
I regressed state severity out and there indeed is a negative relationship that grows stronger
- discontent associated with higher worry - this is further increased by state severity in high TF2 individuals
TF3: Catastrophizing
- catastrophizing is related to generally higher general, economic and closep worry as well as prob estimated and anxiety/avoidance
- however, high CAT are dissociated from state severity in worries and NOT in probability estimates
TF4: Physiological Anxiety
- generally higher closep worry but high PhAnx don't follow objective severity, not economic
- worry is divrced from objective severity
- prob estimates are generally higher but again in high TF4 they don't follow objective severity
TF5: Depression
- interestingly, in depression there isn't neither linear increase nor modulation by state severity, i.e. individuals high in depression track objective severity well emotionally as well as objectively (not quite true, they don't track severity)
- investigate non-linear relationship here (
‼ )
- it also seems that state severity dissociates from economic worry on the second half
TF6: Cognitive anxiety
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in most measures state measures are enhanced by the interaction of cognitive anxiety and state severity but there is no general association
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this interaction is true in closep worry, anxiety/avoidance, econmic worry
-in prob estimates and worry, this interaction also exists, however, there is general negative relationship between estimate and worry and cogAnx
Example for Session 5