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

Coparenting Relationship Trajectories: Marital Violence Linked to Change and Variability After Separation

Associations between marital intimate partner violence (IPV) and postseparation coparenting relationship trajectories were examined among 135 mothers who participated in 5 interviews at 3-month intervals in the year following their divorce filing. Growth curve analysis was conducted to assess change and variability in coparenting dimensions (i.e., conflict, support, communication about child rearing, and harassment) in the overall sample and by type of IPV. In the overall sample, coparenting conflict, communication about child rearing, and harassment decreased across the year following separation. However, coparenting relationships differed considerably based on marital IPV experiences. At Time 1, mothers in relationships with coercive controlling violence (CCV) reported higher levels of harassment and conflict, and lower levels of support and communication about coparenting, than mothers with situational couple violence (SCV) or no violence (NV). Furthermore, coparenting relationship trajectories differed significantly by IPV group, with mothers who experienced CCV showing more variability in conflict and harassment, and more marked changes in conflict, support, and harassment. Despite many similarities, mothers with SCV showed higher initial levels of harassment compared to mothers with NV. Findings can support family court and social service professionals\u27 efforts to individualize interventions with divorcing parents based on IPV experiences. In cases of CCV, for example, attention to heightened control dynamics in the immediate separation period remain critical but the persistent volatility across the first year suggests the potential for chronic stress. With SCV, practitioners may be able to capitalize on parents\u27 reasonable levels of communication and steady coparenting support

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