Measuring objective and subjective well-being: dimensions and data sources

• Published in: International journal of data science and analytics Vol. 11; no. 4; pp. 279 - 309
• Main Authors: Voukelatou, Vasiliki, Gabrielli, Lorenzo, Miliou, Ioanna, Cresci, Stefano, Sharma, Rajesh, Tesconi, Maurizio, Pappalardo, Luca
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.05.2021
• Subjects: A Social Mining and Big Data Ecosystem for Open; Artificial Intelligence; Business Information Systems; Computational Biology/Bioinformatics; Computer Science; Data Mining and Knowledge Discovery; Database Management; Responsible Data Science; Review; Subjective well-being; Objective well-being; Well-being dimensions; Well-being; Artificial intelligence for social good; Data science for social good; New data sources
• ISSN: 2364-415X; 2364-4168
• DOI: 10.1007/s41060-020-00224-2

Well-being is an important value for people’s lives, and it could be considered as an index of societal progress. Researchers have suggested two main approaches for the overall measurement of well-being, the objective and the subjective well-being. Both approaches, as well as their relevant dimensions, have been traditionally captured with surveys. During the last decades, new data sources have been suggested as an alternative or complement to traditional data. This paper aims to present the theoretical background of well-being, by distinguishing between objective and subjective approaches, their relevant dimensions, the new data sources used for their measurement and relevant studies. We also intend to shed light on still barely unexplored dimensions and data sources that could potentially contribute as a key for public policing and social development.