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

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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.

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Data Availability

The dataset analyzed during the current study is publicly available in the World Value Survey (WVS) repository. https://www.worldvaluessurvey.org/wvs.jsp

Code Availability

The code is available in the author’s GitHub repository. https://github.com/ElahehJafarigol

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Author notes

  1. William Keely, Tess Hortag, Tom Welborn, and Peyman Hekmatpour contributed equally to this work.

Authors and Affiliations

  1. Data Science and Analytics Institute, University of Oklahoma, 202 W. Boyd St., Room 409, Norman, OK, 73019, USA

    Elaheh Jafarigol, William Keely, Tess Hortag & Tom Welborn

  2. Department of Sociology, University of Oklahoma, 780 Van Vleet Oval, Kaufman Hall, Room 331, Norman, OK, 73019, USA

    Peyman Hekmatpour

  3. Industrial and Systems Engineering, University of Oklahoma, 202 W. Boyd St., Room 104, Norman, OK, 73019, USA

    Theodore B. Trafalis

Authors
  1. Elaheh Jafarigol
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  2. William Keely
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  3. Tess Hortag
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Correspondence to Elaheh Jafarigol.

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Jafarigol, E., Keely, W., Hortag, T. et al. Religious Affiliation in the Twenty-First Century: A Machine Learning Perspective on the World Value Survey. Soc 60, 733–749 (2023). https://doi.org/10.1007/s12115-023-00887-0

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