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

Joel, Samantha; Eastwick, Paul W; Allison, Colleen J; Arriaga, Ximena B; Baker, Zachary G; Bar-Kalifa, Eran; Bergeron, Sophie; Birnbaum, Gurit E; Brock, Rebecca L; Brumbaugh, Claudia C; Carmichael, Cheryl L; Chen, Serena; Clarke, Jennifer; Cobb, Rebecca J; Coolsen, Michael K; Davis, Jody; de Jong, David C; Debrot, Anik; DeHaas, Eva C; Derrick, Jaye L

doi:10.1073/pnas.1917036117

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Joel, S., Eastwick, P. W., Allison, C. J., Arriaga, X. B., Baker, Z. G., Bar-Kalifa, E., Bergeron, S., Birnbaum, G. E., Brock, R. L., Brumbaugh, C. C., Carmichael, C. L., Chen, S., Clarke, J., Cobb, R. J., Coolsen, M. K., Davis, J., de Jong, D. C., Debrot, A., DeHaas, E. C., … Horn, A. B. (2020). Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies. Proceedings of the National Academy of Sciences of the United States of America, 117, 19061–19071. https://doi.org/10.1073/pnas.1917036117

Abstract

Given the powerful implications of relationship quality for health and well-being, a central mission of relationship science is explaining why some romantic relationships thrive more than others. This large-scale project used machine learning (i.e., Random Forests) to 1) quantify the extent to which relationship quality is predictable and 2) identify which constructs reliably predict relationship quality. Across 43 dyadic longitudinal datasets from 29 laboratories, the top relationship-specific predictors of relationship quality were