Integrated modelling for delineating index of subjective well-being: Psychological predictors and method of response functions

The article presents further development and application of the method of response functions (MRF) for delineating quality-of-life (QoL) indices and indicators. Being a multi-faceted concept QoL embraces not only the material aspects of life like increased wealth measured by higher income, the number of dwellings or availability of basic services and amenities such as water and electricity but also the less tangible aspects of life such as satisfaction with the living environment or a greater sense of happiness or joy. One of the main issues in QoL studies has been the explanation of subjective well-being (SWB). The most replicable, surprising and counter intuitive findings to emerge from the last four decades of research on SWB are the strong influence of personality and relatively weak influence of situational factors. The objectives of the present study were (1) to apply the method of response functions for the construction of purposeful, credible, nonlinear integrated SWB model from data and prior knowledge or information; (2) to obtain highly aggregated SWB index, reflecting the main underlying mechanisms, as the direct output of the SWB model. on theoretical considerations several personality variables (inner-directedness, sociotropy, maladaptive perfectionism (concern over mistakes, doubts about actions), locus of control, body dissatisfaction and alexithymia) were selected as possible predictors of subjective well-being (SWB) level. Selected at random 132 subjects (28 males, 108 females, mean age 36.6 ± 9.7 years) participated in the experimental study. Subjects were assessed with the appropriate surveys and questionnaires. The obtained data were used for the construction of the SWB model – the model of subjective well-being with mentioned above personality variables as predictors. Results revealed that the response functions model not only predicts the level of subjective well-being satisfactorily, but also allows obtaining the highly aggregated SWB index.