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

Fluctuations in Support Across the Transition to Parenthood: Examining 2 Dyadic Longitudinal Studies of First-Time Parents

This project examines mean levels and fluctuations (i.e., within-person variability) in support across the transition to parenthood. We examined (1) who tends to fluctuate more versus less in support and (2) the degree to which fluctuations in support forecast key relationship outcomes across time

Study 2 Information

Data for Study 2 can be requested from the corresponding author. Data were provided, verified, and reproduced during the review process

Study 1 Information

The data from Study 1 are anonymized and centered. To reproduce the results for our LPA analyses anxiety, depressive symptoms, and daily stress need to be uncentered. Please use the descriptive statistics reported in the main text to uncenter this data

Support Mean Levels and Fluctuations

Examining mean levels and fluctuations in social support

Attachment Change during COVID

Sub-project examining changes in attachment from the neutral period (before COVID-19) and threat period (during COVID-19)

Partner predictors of marital aggression across the transition to parenthood: an I3 approach

© The Author(s) 2018. The stress that arises during the transition to parenthood often places significant strain on marriages that can result in marital problems such as aggression victimization. In this research, we use an I 3 framework to identify specific partner variables that are likely to promote physical aggression victimization across the transition to parenthood. Examining both intercepts (i.e., mean levels of aggression victimization estimated at childbirth) and slopes (e.g., changes in aggression victimization estimated over time), we find support for a three-way interaction anticipated by the I 3 framework. Specifically, male partners were more likely to report being the victim of aggression at childbirth and also during the 24 months that followed when their female partner reported experiencing greater parental stress (an instigator to aggression in the I 3 framework), greater relationship-specific attachment avoidance (an impellor to aggression), and lower relationship satisfaction (the lack of an inhibitor to aggression). Implications for the prevention of marital aggression associated with these I 3 factors are discussed