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National Academy of Sciences, Proceedings of the National Academy of Sciences, 32(117), p. 19061-19071, 2020

DOI: 10.1073/pnas.1917036117

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Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies

Journal article published in 2020 by Samantha Joel ORCID, Paul W. Eastwick, Colleen J. Allison, Ximena B. Arriaga ORCID, Zachary G. Baker ORCID, Eran Bar-Kalifa, Sophie Bergeron, Gurit E. Birnbaum ORCID, Rebecca L. Brock, Claudia C. Brumbaugh, Cheryl L. Carmichael ORCID, Serena Chen, Jennifer Clarke, Rebecca J. Cobb, Michael K. Coolsen and other authors.
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

Significance What predicts how happy people are with their romantic relationships? Relationship science—an interdisciplinary field spanning psychology, sociology, economics, family studies, and communication—has identified hundreds of variables that purportedly shape romantic relationship quality. The current project used machine learning to directly quantify and compare the predictive power of many such variables among 11,196 romantic couples. People’s own judgments about the relationship itself—such as how satisfied and committed they perceived their partners to be, and how appreciative they felt toward their partners—explained approximately 45% of their current satisfaction. The partner’s judgments did not add information, nor did either person’s personalities or traits. Furthermore, none of these variables could predict whose relationship quality would increase versus decrease over time.