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Computer Science > Machine Learning

arXiv:2310.10874 (cs)
[Submitted on 16 Oct 2023]

Title:Religious Affiliation in the Twenty-First Century: A Machine Learning Perspective on the World Value Survey

Authors:Elaheh Jafarigol, William Keely, Tess Hartog, Tom Welborn, Peyman Hekmatpour, Theodore B. Trafalis
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Abstract:This paper is a quantitative analysis of the data collected globally by the World Value Survey. The data is used to study the trajectories of change in individuals' religious beliefs, values, and behaviors in societies. Utilizing random forest, we aim to identify the key factors of religiosity and classify respondents of the survey as religious and non religious using country level data. We use resampling techniques to balance the data and improve imbalanced learning performance metrics. The results of the variable importance analysis suggest that Age and Income are the most important variables in the majority of countries. The results are discussed with fundamental sociological theories regarding religion and human behavior. This study is an application of machine learning in identifying the underlying patterns in the data of 30 countries participating in the World Value Survey. The results from variable importance analysis and classification of imbalanced data provide valuable insights beneficial to theoreticians and researchers of social sciences.
Subjects: Machine Learning (cs.LG); Computers and Society (cs.CY)
Cite as: arXiv:2310.10874 [cs.LG]
  (or arXiv:2310.10874v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2310.10874
Journal reference: Society, pp.1-17 (2023)
Related DOI: https://doi.org/10.1007/s12115-023-00887-0

Submission history

From: Elaheh Jafarigol [view email]
[v1] Mon, 16 Oct 2023 23:01:16 UTC (1,458 KB)
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