Developing a Structural Model of Psychological Wellbeing Based on Optimism and Positive Thinking with Emotion Regulation Mediation

Background & Objectives: Currently, despite all the scientific advances that today's human society is undergoing, i.e., rapid and comprehensive changes, especially respecting culture and lifestyle, numerous individuals lack the necessary skills to cope with problems and even manage the routine issues of their lives. Being vulnerable to stressors, they are also prone to various bio psychological disorders. Additionally, components, such as Psychological Wellbeing (PWB), life satisfaction, hope, happiness, optimism, and other positive traits, including essential constructs and indicators of mental health in positive psychology are less experienced. The present study aimed to develop a structural model of PWB based on optimism, and positivity, with Emotion Regulation (ER) mediation. Methods: The present descriptive–correlational study employed structural equation modeling. The statistical population of the present study included all postgraduate students (MS & PhD) in the Faculty of Humanities and Social Sciences, Science and Research Branch, Islamic Azad University, Tehran City, Iran. The total population of the study was 5353 subjects (3732 seniors & 1621 doctoral students). To determine the sample size, 10–15 individuals were required for modeling; based on the available variables, 358 individuals were selected by stratified random sampling technique. Therefore, 358 postgraduate students were selected as the study sample. The sampling was proportional to the number of postgraduate and doctoral students. Initially, the list of undergraduate and postgraduate students was obtained. Then, according to the number of undergraduate students per faculty, 358 subjects were randomly selected. The required data were obtained using the Psychological Wellbeing Scale (Ryff, 1989), the Revised Life Orientation Test (Scheier & Carver, 1994), the Positive Thinking Questionnaire (Ingram & Wisnicki, 1988), and the Emotion Regulation Questionnaire (Gross & John, 2003). This study used descriptive statistics to categorize the demographic characteristics of the study subjects to calculate frequency, percentage, mean, and standard deviation values. Furthermore, inferential statistics were used to analyze the collected results. The Kolmogorov–Smirnov test was used to identify the data normality and Pearson correlation coefficient and structural equation modeling were also implemented in this research. Data analysis was conducted by SPSS and AMOS at the significance level of 0.05. Results: The present research results indicated that the total path coefficient (the sum of direct and indirect path coefficients) between optimism and PWB was positive and significant (β=0.37, p<0.001); the total path coefficients between positive thinking and PWB were positive and significant (β=0.44, p<0.001). Additionally, the direct path coefficient between ER and PWB was positive and significant (β=0.30, p<0.001); the path coefficients between optimism and PWB (β=0.13, p<0.001), positive thinking and PWB (β=0.29, p<0.001), optimism and ER (β=0.24, p<0.001), as well as positive thinking and ER (β=0.50, p<0.001) were positive and significant. Finally, the indirect pathway coefficient between optimism and PWB with the mediating role of ER was positive and significant (β=0.07, p<0.001). Moreover, the indirect pathway coefficient between positive thinking and PWB with the mediating role of ER was positive and significant (β=0. 15, p<0.001). Goodness of fit indices also supported the optimal fit of the model with the collected data (/df =2.67, CFI=0.98, GFI=0.93, AGFI=0.91, RMSEA=0.068). Conclusion: Optimism through ER provided a positive and significant indirect effect on PWB in the study subjects. Positive thinking through ER indicated a positive and significant indirect effect on PWB in the study participants.