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.

Attachment orientations in adulthood can change over time, but the specific circumstances that directly affect change are not well understood. Bowlby proposed that those circumstances involve the assimilation of information that is incongruent with an individual’s existing attachment orientation and underlying working models. In this study, 137 couples transitioning to parenthood were followed across the first 2 years of their firstborn child’s life, with both partners providing data at five time-points. Only changes in attachment avoidance were examined in this study. Consistent with predictions, downward changes in avoidance were associated with relationship events that introduced information inconsistent with avoidant working models. For example, people who provided more support to their partners declined in avoidance across the transition period. We discuss these findings and new directions needed to better understand when and how attachment orientations change during major life transitions.

Attachment anxiety can decline in relationships but little is known about how or why. A new framework—the Attachment Security Enhancement Model (ASEM)—suggests that what allays current (momentary) insecurity may not necessarily reduce attachment anxiety across time. This article differentiates momentary versus extended attachment processes by examining concurrent versus longitudinal associations. Cohabitating partners ( N = 137 couples) were examined over a 2-year period as they became first-time parents, a transition that could change attachment tendencies. Consistent with ASEM predictions: (1) Anxiously attached spouses who perceived more proximal and sensitive reassurance from their partners felt less concurrent attachment anxiety but not less anxiety across time, and (2) attachment anxiety declined across time when spouses derived personal competence and self-efficacy from their new parenting role. These results document an important distinction between mitigating insecure thoughts and feelings that might reinforce attachment anxiety, versus encountering new experiences that may actually revise chronic insecurity.