GRATITUDE AND LONELINESS AS PREDICTORS OF SUBJECTIVE WELL-BEING

Abstract

The main aim of this research is to analyze the role of loneliness found in previous studies as well as to examine the role of gratitude as one of the main factors contributing to subjective well-being. The sample is a convenience sample and consists of 219 respondents (78.99% women). The instruments used in this research were: the Social and Emotional Loneliness Scale for Adults (SELSA; di Tomasso & Spinner, 1993), the Life satisfaction scale (SWLS; Diener, Emmons, Larsen, & Griffin, 1985), PANAS (Watson, Clark, & Tellegan, 1988) and The Gratitude Questionnaire (McCullough et al., 2002). The data were analyzed using a hierarchical linear regression where the criterion variables were components of subjective well-being, the predictor in the first step gratitude, and predictors in the second step three types of loneliness. The results of this research show that the only statistically significant predictor of positive affects is gratitude (model 1: β=.281, p<.01; model 2: β=.243. p<.01). Gratitude and loneliness explain 30.1% of the variance of positive affect. When it comes to negative affect, the first model explains 12.6% of the variance, while the second model explains 21.9% of the total variance. Only the second model is statistically significant (p<.05). The only statistically significant predictor is loneliness in the family (β=.143. p=.049). Finally, when it comes to life satisfaction, the first model explains 23.5% of the variance while the second model explains 38.6% of the total variance. When loneliness is added in the second model, an additional 15.1% of the statistically significant explained variance appears (p<.01). The best predictor in this regression analysis is social loneliness (β=-.297, p<.01). Based on these results we can conclude that both gratitude and loneliness are important variables for subjective well-being – but that some are more important for some and some for other aspects of subjective well-being.