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

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.

Registered Replication Report: Study 1 From Finkel, Rusbult, Kumashiro, & Hannon (2002).

Finkel, Rusbult, Kumashiro, and Hannon (2002, Study 1) demonstrated a causal link between subjective commitment to a relationship and how people responded to hypothetical betrayals of that relationship. Participants primed to think about their commitment to their partner (high commitment) reacted to the betrayals with reduced exit and neglect responses relative to those primed to think about their independence from their partner (low commitment). The priming manipulation did not affect constructive voice and loyalty responses. Although other studies have demonstrated a correlation between subjective commitment and responses to betrayal, this study provides the only experimental evidence that inducing changes to subjective commitment can causally affect forgiveness responses. This Registered Replication Report (RRR) meta-analytically combines the results of 16 new direct replications of the original study, all of which followed a standardized, vetted, and preregistered protocol. The results showed little effect of the priming manipulation on the forgiveness outcome measures, but it also did not observe an effect of priming on subjective commitment, so the manipulation did not work as it had in the original study. We discuss possible explanations for the discrepancy between the findings from this RRR and the original study.