Religious Affiliation in the Twenty-First Century: A Machine Learning
  Perspective on the World Value Survey
Elaheh Jafarigol William Keely Tess Hartog Tom Welborn Peyman Hekmatpour Theodore B. Trafalis
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.