Joel et al (2020). Machine Learning Uncovers the Most Robust Self-Report Predictors of Relationship Quality Across 43 Longitudinal Couples Studies. Accepted Version.[12].pdf (1.18 MB)
Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies
journal contribution
posted on 2023-06-10, 05:44 authored by Samantha Joel, Paul W Eastwick, Colleen J Allison, Ximena B Arriaga, Zachary G Baker, Eran Bar-Kalifa, Sophie Bergeron, Gurit E Birnbaum, Rebecca L Brock, Claudia C Brumbaugh, Cheryl L Carmichael, Serena Chen, Jennifer Clarke, Rebecca J Cobb, Mariko VissermanMariko Visserman, othersGiven 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 perceived-partner commitment, appreciation, sexual satisfaction, perceived-partner satisfaction, and conflict. The top individual-difference predictors were life satisfaction, negative affect, depression, attachment avoidance, and attachment anxiety. Overall, relationship-specific variables predicted up to 45% of variance at baseline, and up to 18% of variance at the end of each study. Individual differences also performed well (21% and 12%, respectively). Actor-reported variables (i.e., own relationship-specific and individual-difference variables) predicted two to four times more variance than partner-reported variables (i.e., the partner’s ratings on those variables). Importantly, individual differences and partner reports had no predictive effects beyond actor-reported relationship-specific variables alone. These findings imply that the sum of all individual differences and partner experiences exert their influence on relationship quality via a person’s own relationship-specific experiences, and effects due to moderation by individual differences and moderation by partner-reports may be quite small. Finally, relationship-quality change (i.e., increases or decreases in relationship quality over the course of a study) was largely unpredictable from any combination of self-report variables. This collective effort should guide future models of relationships.
History
Publication status
- Published
File Version
- Accepted version
Journal
Proceedings of the National Academy of SciencesISSN
0027-8424Publisher
Proceedings of the National Academy of SciencesExternal DOI
Issue
32Volume
117Page range
19061-19071Department affiliated with
- Psychology Publications
Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2023-01-03First Open Access (FOA) Date
2023-02-10First Compliant Deposit (FCD) Date
2023-02-10Usage metrics
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