A fuzzy logic modelling approach on psychological data

Abstract

Meeting basic psychological needs could be difficult to maintain in current pandemic times, mainly due to preventive measures involving social distancing or full quarantine, which seem to play a very important role in well-being. The theory of basic psychological needs is a sub-theory of human motivation theory known as the theory of self-determination. This theory argues that meeting the needs of autonomy, relatedness and competence is crucial for motivation, optimal development, efficient functioning and health. Several research, examining the effects of basic psychological needs on well-being, concluded that changes in meeting the three needs had a significant effect on well-being. Because perceived stress plays a vital role in daily life, several coping strategies have been shown to effectively manage stress and reduce its negative consequences. In this study, coping mechanisms refer to both cognitive and behavioral efforts to alleviate or overcome stressful situations, especially when an automatic response is not readily available. The present study aims to examine a predictive model of competence need satisfaction based on adaptive coping mechanisms: active coping and positive reframing, on a convenience sampling of 403 Romanian respondents. Results show that 3% of the variance in competence need satisfaction is explained by active coping and positive reframing. In this work, we have used fuzzy logic modelling on our psychological data to deal with the imprecision and vagueness inherent in input data and build a more reliable model for estimating psychological variables relations. Implications and conclusions are discussed.