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

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,

SUBMITTER: Joel S 

PROVIDER: S-EPMC7431040 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature