Segmentation of Adult Respondents’ Well-being Profiles Based on Daily Stress, Social Networks, Personal Resources, and Lifestyle Using Clustering Method

Abstract

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.

References

Andretta, J. R., & McKay, M. T. (2020). Self-efficacy and well-being in adolescents: A comparative study using variable and person-centered analyses. Children and Youth Services Review, 118, 105374. https://doi.org/10.1016/j.childyouth.2020.105374

Chong, B. (2021). K-means clustering algorithm: A brief review. Academic Journal of Computing & Information Science, 4(5), 37–40. https://doi.org/10.25236/AJCIS.2021.040506

Delimiro Visbal-Cadavid, A. M.-M. (2020). Use of Factorial Analysis of Mixed Data (FAMD) and Hierarchical Cluster Analysis on Principal Component (HCPC) for Multivariate Analysis of Academic Performance of Industrial Engineering Programs. Journal of Southwest Jiaotong University, 55(5). https://jsju.org/index.php/journal/article/view/745

Grippo, C., Jagmohan, P., Helbich, T. H., Kapetas, P., Clauser, P., & Baltzer, P. A. T. (2021). Correct determination of the enhancement curve is critical to ensure accurate diagnosis using the Kaiser score as a clinical decision rule for breast MRI. European Journal of Radiology, 138, 109630. https://doi.org/10.1016/j.ejrad.2021.109630

Hennessey, A., MacQuarrie, S., & Petersen, K. J. (2024). Exploring physical, subjective and psychological wellbeing profile membership in adolescents: A latent profile analysis. BMC Psychology, 12(1), 720. https://doi.org/10.1186/s40359-024-02196-5

Lazić, M., Jovanović, V., Gavrilov‐Jerković, V., & Boyda, D. (2021). A person‐centered evaluation of subjective well‐being using a latent profile analysis: Associations with negative life events, distress, and emotion regulation strategies. Stress and Health, 37(5), 962–972. https://doi.org/10.1002/smi.3056

Leite, Â., Ramires, A., Vidal, D. G., Sousa, H. F. P. E., Dinis, M. A. P., & Fidalgo, A. (2021). Hierarchical Cluster Analysis of Human Value Priorities and Associations with Subjective Well-Being, Subjective General Health, Social Life, and Depression across Europe. Social Sciences, 10(2), 74. https://doi.org/10.3390/socsci10020074

Palacios, C. A., Reyes-Suárez, J. A., Bearzotti, L. A., Leiva, V., & Marchant, C. (2021). Knowledge Discovery for Higher Education Student Retention Based on Data Mining: Machine Learning Algorithms and Case Study in Chile. Entropy, 23(4). https://doi.org/10.3390/e23040485

Pasanen, T. P., Tamminen, N., Martelin, T., Mankinen, K., & Solin, P. (2021). Profiles of subjective health among people living alone: A latent class analysis. BMC Public Health, 21(1), 1335. https://doi.org/10.1186/s12889-021-11396-2

Saputra, D. M., Saputra, D., & Oswari, L. D. (2020). Effect of Distance Metrics in Determining K-Value in K-Means Clustering Using Elbow and Silhouette Method. 341–346. https://doi.org/10.2991/aisr.k.200424.051

Singh, A., Pandey, U. K., Singh, A., Bhatt, H., Mishra, S., & Mishra, V. K. (2024). Improvising Performance of Machine Learning Algorithm using FAMD on Coronary Artery Disease. 2024 International Conference on Control, Computing, Communication and Materials (ICCCCM), 569–574. https://doi.org/10.1109/ICCCCM61016.2024.11039854

Ufeli, C. P., Sattar, M. U., Hasan, R., & Mahmood, S. (2025). Enhancing Customer Segmentation Through Factor Analysis of Mixed Data (FAMD)-Based Approach Using K-Means and Hierarchical Clustering Algorithms. Information, 16(6). https://doi.org/10.3390/info16060441

UNESCO. (2022). UNESCO strategy on education for health and well-being. UNESCO. https://doi.org/10.54675/MSST2323

Wu, M., Yang, C., Zhang, Y., Umeda, M., Liao, J., & Mawditt, C. (2024). Longitudinal patterns and sociodemographic profiles of health-related behaviour clustering among middle-aged and older adults in China and Japan. Ageing & Society, 44(11), 2464–2483. https://doi.org/10.1017/S0144686X2200143X

Wu, W.-T., Li, Y.-J., Feng, A.-Z., Li, L., Huang, T., Xu, A.-D., & Lyu, J. (2021). Data mining in clinical big data: The frequently used databases, steps, and methodological models. Military Medical Research, 8(1), 44. https://doi.org/10.1186/s40779-021-00338-z