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.

Character strengths and performance outcomes among military brat and non-brat cadets.

Although many studies have compared military vs. civilian samples on a wide variety of characteristics, few have examined these differences within the context of those who commit a portion of their life to the military. In this study, we explored how West Point cadets with ("military brat cadet") or without ("non-brat cadet") a family military background might differ in terms of their character strengths. Although the cadets shared many similarities, we found that several strengths related to self-control were higher in non-brat cadets than brat cadets and that many of these self-control-related strengths were important predictors of performance for brat cadets (but not non-brat cadets). For non-brat cadets, strengths related to a drive to fully involve themselves and navigate relationships with others were better predictors of performance. In a second study utilizing a different class of cadets, we again found support for the idea that nonmilitary brat cadets possessed more self-control than military brat cadets. Better understanding the unique strengths and weaknesses of those within the military who have vs. don't have a military background may provide important insights for future recruitment, training, and military preparation.

Attachment and capitalizing on positive events.

Although previous work has examined how individual differences in attachment security affect support processes for negative events, little work has looked at how attachment security affects support for positive events (capitalization). In a 10-day diary study of romantic couples, we examined the association between individual differences in attachment security and perceptions of capitalization support. We also examined how attachment security moderated the relationship between capitalization support and daily emotions, relationship satisfaction, and life satisfaction. The results showed that stronger avoidance orientations were associated with reduced perceptions of partner responsiveness. Anxiety moderated the association between responsive support and daily outcomes; for those high (versus low) on anxiety, daily relationship and life satisfaction were more strongly tied to partners' capitalization responses, and more negative emotions were experienced regarding those responses. Overall, insecure attachment was associated with mixed reactions to receiving responsive capitalization support.

Safely testing the alarm: close others' responses to personal positive events.

Previous research has shown that receiving social support in the face of negative events (i.e., enacted support) is sometimes correlated with positive outcomes, sometimes unrelated to outcomes, and sometimes associated with negative outcomes. However, people's perception that they have high-quality support available to them when they have a stressor (i.e., perceived support) is consistently and strongly associated with better health, well-being, and relationship functioning. However, both enacted and perceived support available in response to positive event disclosures are consistently associated with positive outcomes. In 2 studies, we examined why enacted support for negative events has such a spotty record and compared it with enacted support for positive events; a third study examined how support for positive events may be a major contributor to perceived availability of effective support for negative events. The results showed that providing responsive support to negative events is particularly difficult; received support for negative events disclosures (but not positive event disclosures) involves substantial drawbacks and risks, especially when that support is not responsive to the recipient's needs; and that enacted support for positive events was a better predictor of later perceptions of the quality of available support for stressors than enacted support for negative events. Findings are discussed in terms of implications for the social support literature and how positive relationship processes influence health and well-being, not only directly but also indirectly by providing critical information regarding the availability of others if a problem occurs.