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.

The living anonymous kidney donor: lunatic or saint?

Studies indicate that 11% to 54% of individuals surveyed would consider donating a kidney, while alive, to a stranger. The idea of 'living anonymous donors' (LADs) as a donor source, however, has not been embraced by the medical community. Reservations focus on the belief that LADs might be psychologically unstable and thus unsuitable donors. Our goal was to inform policy development by exploring the psycho-social make up and motivations of the LAD. Ninety-three unsolicited individuals contacted our center expressing interest in living anonymous donation. Of these, 43 participated in our study, completing two extensive inventories of psychopathology and personality disorder and taking part in the Comprehensive Psycho-Social Interview (CPSI). From the Personality Assessment Inventory (PAI), the revised NEO Personality Inventory (NEO PI-R), and the CPSI, coders assessed psychological health, psycho-social suitability, commitment, and motivations. Twenty-one participants passed the stringent criteria to be considered potential LADs. Content analysis of motivations showed that potential LADs were more likely than non-LADs (those who did not pass the criteria) to have a spiritual belief system and to be altruistic. Non-LADs were more likely than potential LADs to use donation to make a statement against their families. The authors conclude with a preliminary outline of eight policy recommendations.