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

Details

Serval ID
serval:BIB_66734A076851
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies
Journal
Proceedings of the National Academy of Sciences
Author(s)
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., Wolf Scott
ISSN
0027-8424
1091-6490
ISSN-L
0027-8424
Publication state
Published
Issued date
11/08/2020
Peer-reviewed
Oui
Volume
117
Number
32
Pages
19061-19071
Language
english
Notes
Publication types: Journal Article
Publication Status: aheadofprint
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 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.
Keywords
Multidisciplinary
Pubmed
Web of science
Create date
11/08/2020 10:53
Last modification date
19/09/2020 6:21
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