Abstract
BACKGROUND: Stress is among the leading causes for diseases. The assessment of subjectively perceived stress is essential for resilience research. While the Perceived Stress Scale (PSS) is a widely used questionnaire, a German short version of the scale is not yet available. In the current study, we developed such a short version using a machine learning approach for item reduction to facilitate the simultaneous optimization of multiple psychometric criteria. METHOD: We recruited 1,437 participants from an online panel, who completed the German long version of the PSS along with measures of mental health and resilience. An ant-colony-optimization algorithm was used to select items, taking reliability, and construct validity into account. Findings on validity were visualized by psychological network models. RESULTS: We replicated a bifactor structure for the long version of the PSS and derived a two-factor German short version of the PSS with four items, the PSS-2&2. Its factors helplessness and self-efficacy showed differential associations with mental health indicators and resilience-related factors, with helplessness being mainly linked to mental distress. CONCLUSION: The valid and economic short version of the PSS lends itself to be used in future resilience research. Our findings highlight the importance of the two-factor structure of the PSS short versions and challenge the validity of commonly used one-factor models. In cases where the general stress factor is of interest, researchers should use the longer versions of the PSS that allow for the interpretation of total scores, while the PSS-2&2 allows of an economic assessment of the PSS factors helplessness and self-efficacy.