Joel S, Eastwick PW, Allison CJ, Arriaga XB, Baker ZG, Bar-Kalifa E, Bergeron S, Birnbaum GE, Brock RL, Brumbaugh CC, Carmichael CL, Chen S, Clarke J, Cobb RJ, Coolsen MK, Davis J, de Jong DC, Debrot A, DeHaas EC, Derrick JL, Eller J, Estrada MJ, Faure R, Finkel EJ, Fraley RC, Gable SL, Gadassi-Polack R, Girme YU, Gordon AM, Gosnell CL, Hammond MD, Hannon PA, Harasymchuk C, Hofmann W, Horn AB, Impett EA, Jamieson JP, Keltner D, Kim JJ, Kirchner JL, Kluwer ES, Kumashiro M, Larson G, Lazarus G, Logan JM, Luchies LB, MacDonald G, Machia LV, Maniaci MR, Maxwell JA, Mizrahi M, Muise A, Niehuis S, Ogolsky BG, Oldham CR, Overall NC, Perrez M, Peters BJ, Pietromonaco PR, Powers SI, Prok T, Pshedetzky-Shochat R, Rafaeli E, Ramsdell EL, Reblin M, Reicherts M, Reifman A, Reis HT, Rhoades GK, Rholes WS, Righetti F, Rodriguez LM, Rogge R, Rosen NO, Saxbe D, Sened H, Simpson JA, Slotter EB, Stanley SM, Stocker S, Surra C, Ter Kuile H, Vaughn AA, Vicary AM, Visserman ML, and Wolf S
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 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., Competing Interests: The authors declare no competing interest.