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.; Eller, Jami; Estrada, Marie-Joelle; Faure, Ruddy; Finkel, Eli J.; Fraley, R. Chris; Gable, Shelly L.; Gadassi-Polack, Reuma; Girme, Yuthika U.; Gordon, Amie M.; Gosnell, Courtney L.; Hammond, Matthew D.; Hannon, Peggy A.; Harasymchuk, Cheryl; Hofmann, Wilhelm; Horn, Andrea B.; Impett, Emily A.; Jamieson, Jeremy P.; Keltner, Dacher; Kim, James J.; Kirchner, Jeffrey L.; Kluwer, Esther S.; Kumashiro, Madoka; Larson, Grace; Lazarus, Gal; Logan, Jill M.; Luchies, Laura B.; MacDonald, Geoff; Machia, Laura V.; Maniaci, Michael R.; Maxwell, Jessica A.; Mizrahi, Moran; Muise, Amy; Niehuis, Sylvia; Ogolsky, Brian G.; Oldham, C. Rebecca; Overall, Nickola C.; Perrez, Meinrad; Peters, Brett J.; Pietromonaco, Paula R.; Powers, Sally I.; Prok, Thery; Pshedetzky-Shochat, Rony; Rafaeli, Eshkol; Ramsdell, Erin L.; Reblin, Maija; Reicherts, Michael; Reifman, Alan; Reis, Harry T.; Rhoades, Galena K.; Rholes, William S.; Righetti, Francesca; Rodriguez, Lindsey M.; Rogge, Ron; Rosen, Natalie O.; Saxbe, Darby; Sened, Haran; Simpson, Jeffry A.; Slotter, Erica B.; Stanley, Scott M.; Stocker, Shevaun; Surra, Cathy; Ter Kuile, Hagar; Vaughn, Allison A.; Vicary, Amanda M.; Visserman, Mariko L. and Wolf, Scott. 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, 117(32), pp. 19061-19071. ISSN 0027-8424 [Article]

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Abstract or Description

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.

Item Type:

Article

Identification Number (DOI):

https://doi.org/10.1073/pnas.1917036117

Additional Information:

Data deposition: Analysis plans, final syntax files, and word files outlining any preregistration changes can be found for each of the datasets compiled for this report in the Open Science Framework (https://osf.io/d6ykr/). Meta-analytic materials and data, including the final master list of predictors and the syntax used to compute success rates, are also available on the Open Science Framework (https://osf.io/v5e34/). Results for each individual dataset can be found at https://osf.io/4pbfh/.

This article contains supporting information online at https://www.pnas.org/lookup/suppl/doi:10.1073/pnas.1917036117/-/DCSupplemental

Keywords:

romantic relationships, relationship quality, machine learning, Random Forests, ensemble methods

Departments, Centres and Research Units:

Psychology

Dates:

DateEvent
27 April 2020Accepted
27 July 2020Published Online
11 August 2020Published

Item ID:

29101

Date Deposited:

28 Jul 2020 15:39

Last Modified:

29 Jan 2021 21:55

Peer Reviewed:

Yes, this version has been peer-reviewed.

URI:

https://research.gold.ac.uk/id/eprint/29101

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