Search

This study focused on the interaction of demographics and well-being. Diener's subjective well-being (SWB) was successfully validated with Exploratory Graph Analysis and Confirmatory Factor Analysis to track well-being differences of the COVID-19 quarantined individuals. Six tree-based Machine Learning models were trained to classify top 25% SWB scorers during COVID-19 quarantine, after data-splitting (train 70%, test 30%). The model input variables were demographics, to avoid overlapping of inputs-outputs. A 10-fold cross-validation method (70%–30%) was then implemented in the training session to select the optimal Machine Learning model among the six tested. A CART classification was the optimal algorithm (Train-Accuracy = 0.77, Test-Accuracy = 0.75). A clean, three-node tree suggested that if someone spends time on perceived creative activities during the COVID-19 quarantine, under clearly described conditions, he/she had high probabilities to be a top subjective well-being scorer. The key importance of creative activities was subsequently cross-validated with three different model configurations: (1) a different tree-based model (Test-Accuracy =0.75); (2) a different operationalization of subjective well-being (Test-Accuracy =0.75) and (3) a different construct (depression; Test-Accuracy =0.73). This is an integrative approach to study individual differences in subjective well-being, bridging Exploratory Graph Analysis and Machine Learning in a single research cycle with multiples cross-validations. • To combine Machine Learning, Network Psychometrics & CFA is novel research approach. • Offers theory-driven evidence on SWB differences in early COVID-19 quarantine • Adds a newly invented Machine Learning research cycle • Eliminates self-report bias by using demographic inputs in Machine Learning models • Multiple cross-validations with different model configurations strengthen findings. [ABSTRACT FROM AUTHOR]

Using five independent non-clinical cross-cultural samples (total N = 3649; overall M age = 29.31; 31% male and 69% female), this study explored the extent to which Dark Triad traits were indirectly associated with symptoms of psychopathology through mental toughness. Although Machiavellianism and psychopathy have not been studied extensively in this context, previous research (both cross-sectional and longitudinal) reports that grandiose narcissism increases mental toughness contributing indirectly to positive outcomes such as lower anxiety, stress, and depression. Accordingly, this study examined Machiavellianism, psychopathy, and narcissism in the context of mental toughness and psychopathology. A particular focus was placed on investigating negative relationships between grandiose narcissism and psychopathology. Participants completed self-report measures assessing the Dark Triad, mental toughness, and psychopathology. In all samples, grandiose narcissism exerted moderate negative, indirect associations with anxiety, stress, and depression through mental toughness. Relationships between Machiavellianism and psychopathy and psychopathology were generally weak and positive but varied across countries. Findings provided further cross-cultural support for a mediation model in which grandiose narcissism is related to higher mental toughness and lower psychopathology. Outcomes from this study indicate that exploration of the link between grandiose narcissism and resilience traits such as mental toughness can provide important conceptual insights into the adaptive properties of narcissism, and help to explain why grandiose narcissism is associated with a decrease in some psychopathological symptoms. [ABSTRACT FROM AUTHOR]