Authors
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, Michael K Coolsen, Jody Davis, David C de Jong, Anik Debrot, Eva C DeHaas, Jaye L Derrick, Jami Eller, Marie-Joelle Estrada, Ruddy Faure, Eli J Finkel, R Chris Fraley, Shelly L Gable, Reuma Gadassi-Polack, Yuthika U Girme, Amie M Gordon, Courtney L Gosnell, Matthew D Hammond, Peggy A Hannon, Cheryl Harasymchuk, Wilhelm Hofmann, Andrea B Horn, Emily A Impett, Jeremy P Jamieson, Dacher Keltner, James J Kim, Jeffrey L Kirchner, Esther S Kluwer, Madoka Kumashiro, Grace Larson, Gal Lazarus, Jill M Logan, Laura B Luchies, Geoff MacDonald, Laura V Machia, Michael R Maniaci, Jessica A Maxwell, Moran Mizrahi, Amy Muise, Sylvia Niehuis, Brian G Ogolsky, C Rebecca Oldham, Nickola C Overall, Meinrad Perrez, Brett J Peters, Paula R Pietromonaco, Sally I Powers, Thery Prok, Rony Pshedetzky-Shochat, Eshkol Rafaeli, Erin L Ramsdell, Maija Reblin, Michael Reicherts, Alan Reifman, Harry T Reis, Galena K Rhoades, William S Rholes, Francesca Righetti, Lindsey M Rodriguez, Ron Rogge, Natalie O Rosen, Darby Saxbe, Haran Sened, Jeffry A Simpson, Erica B Slotter, Scott M Stanley, Shevaun Stocker, Cathy Surra, Hagar Ter Kuile, Allison A Vaughn, Amanda M Vicary, Mariko L Visserman, Scott Wolf
Publication date
2020/8/11
Journal
Proceedings of the National Academy of Sciences
Volume
117
Issue
32
Pages
19061-19071
Publisher
National Academy of Sciences
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 …
Scholar articles
S Joel, PW Eastwick, CJ Allison, XB Arriaga, ZG Baker… - Proceedings of the National Academy of Sciences, 2020