A machine learning model of cultural change: Role of prosociality, political attitudes, and Protestant work ethic.

Abstract

What attitudes, values, and beliefs serve as key markers of cultural change? To answer this question, we examined 221,485 respondents from the World Values Survey, a multiwave cross-country survey of people's attitudes, values, and beliefs. We trained a machine learning model to classify respondents into seven waves (i.e., periods). Once trained, the machine learning model identified a separate group of 24,611 respondents' wave with a balanced accuracy of 77%. We then queried the model to identify the attitudes, values, and beliefs that contributed the most to its classification decisions, and therefore, served as markers of cultural change. These included religiosity, social attitudes, political attitudes, independence, life satisfaction, Protestant work ethic, and prosociality. Although past research in cultural change has discussed decreasing religiosity and increasing liberalism and independence, it has not yet identified Protestant work ethic, political orientation, and prosociality as values relevant to cultural change. Thus, the current research points to new directions for future research on cultural change that might not be evident from either a deductive or an inductive approach. This research illustrates that the abductive approach of machine learning, which focuses on the most likely explanations for an outcome, can help generate novel insights. (PsycInfo Database Record (c) 2021 APA, all rights reserved).