Segmentation of Adult Respondents’ Well-being Profiles Based on Daily Stress, Social Networks, Personal Resources, and Lifestyle Using Clustering Method - Ajang Sopandi; Siti Ummi Masruroh; Neneng Tati Sumiati; Cindy Rahayu; Rona Nisa Sofia Amriza; Doni Febrian (The Journal of Indonesia Sustainable Development Planning, 2026)
Existing literature on the topic of wellbeing mostly utilized scales and methods that are abstract and variable-centered yet assume homogeneity within the population being studied. This research utilizes a person-centered approach to classify the sample of 15,977 adults from a large-scale online survey about their wellbeing according to variables related to their stress, social networks, personal resources, and lifestyle. Factor analysis of mixed data (FAMD) is performed to reduce 22 variables of mixed types to 14 principal components that account for 77.51% of the variance in the data. Using these components, eight segments of well-being are classified by K-Means clustering and validated using Silhouette analysis. These segments range from those with low levels of stress, high levels of meditation, and clear goals for their lives to those with high levels of stress, no sense of accomplishment in their careers, and few social connections outside of work. Interestingly, another variable that was revealed as significantly different within each of the stress levels groups was the notion of whether or not the individual feels like they have enough money to cover their needs. Finally, the methods used in this research can be replicated to evaluate the wellbeing of the general population and to inform the creation of interventions to improve the lives of those with certain types of wellbeing profiles.