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.

Empathic accuracy (EA; Ickes & Hodges, 2013) is the extent to which people accurately perceive their peers' thoughts, feelings, and other inner mental states. EA has particularly interested researchers in the context of romantic couples. Reviews of the literature suggest a possible link between romantic partners' EA and their relationship satisfaction (Ickes & Simpson, 2001; Sillars & Scott, 1983). To assess the magnitude of this association and examine possible moderators, we performed a meta-analytic review of 21 studies (total N = 2,739 participants) that examined the association between EA and satisfaction. We limited our review to studies measuring EA using the dyadic interaction paradigm (Ickes, Stinson, Bissonnette, & Garcia, 1990). We found a small but significant association between the two (r = .134, p < .05). Subsequent moderation analyses demonstrated that EA for negative emotions (one's accuracy when assessing a partner's negative emotions) was more closely related to satisfaction (r = .171, p < .05) than EA for positive ones (r = .068, p > .1). The association was also stronger in relationships of moderate length, suggesting that EA may be more meaningful when relationships are consolidating but before they become stable. Gender and procedural variations on the dyadic interaction paradigm did not moderate the association, and there was no difference depending on whether the association was between EA and perceivers' or targets' satisfaction (i.e., actor or partner effects). We discuss the implications of these findings and offer recommendations for future EA studies. (PsycINFO Database Record

Adolescence is a critical period for social development, which COVID-19 has dramatically altered. Quarantined youths had limited in-person interactions with peers. The present study used an intensive longitudinal design to investigate changes in interpersonal dynamics and mental health during COVID-19. Specifically, we investigated whether the associations between different social contexts-that is, "spillover"-changed during COVID-19 and whether changes in social interactions during COVID-19 was associated with changes in depressive symptoms. Approximately 1 year prior to the onset of COVID-19, 139 youths reported depressive symptoms and daily interactions with parents, siblings, and friends, every day for 21 days via online questionnaires. Shortly after schools closed due to COVID-19, 115 of these youths completed a similar 28-day diary. Analyses included 112 youths (62 girls; 73% Caucasian; M age = 11.77, range = 8 to 15 in Wave 1) who completed at least 13 diary days in each data wave. Our results show that younger adolescents experienced significant decreases in negative and positive interactions with friends, whereas older adolescents showed significant decreases in negative interactions with friends and significant increases in positive interactions with siblings. As predicted, within-day spillover of positive interactions and person-level association of negative interactions increased within the family during COVID-19, whereas within-day spillover of positive interactions between family and friends decreased. We also found a dramatic increase in depressive symptoms. More negative interactions and fewer positive interactions with family members were associated with changes in depressive symptoms. Our study sheds light on how youths' social development may be impacted by COVID-19.

Intensive longitudinal methods (ILMs), in which data are gathered from participants multiple times with short intervals (typically 24 hours or less apart), have gained considerable ground in personality research and may be useful in exploring causality in both classic personality trait models and more novel contextualized personality state models. We briefly review the various terms and uses of ILMs in various fields of psychology and present five main strategies that can help researchers infer causality in ILM studies. We discuss the use of temporal precedence to establish causality, through both lagged analyses and natural experiments; the use of external measures and peer reports to go beyond self‐report data; delving deeper into repeated measures to derive new indices; the use of contextual factors occurring during the measurement period; and combining experimental methods and ILMs. These strategies are illustrated by examples from existing research and by new empirical findings from two dyadic daily diary studies (N = 80 and N = 108 couples) and an experience sampling method study of personality states (N = 52). We conclude by offering a short checklist for designing ILM studies with causality in mind and look at the applicability of these strategies in the intersection of personality psychology and other psychological research domains. Copyright © 2018 European Association of Personality Psychology

Recent research on empathy finds evidence for 2 different pathways that enable individuals to accurately infer other persons' inner mental states: an automatic, indirect pathway that operates by having a mental state similar to the target's and (correctly) assuming that this state is similar to the target's, and a more controlled direct pathway that involves assessing the target's mental state with no regard for one's own. We present 3 daily diary studies (N = 53, 38 and 80 couples) examining the contribution of these pathways to empathic accuracy in daily assessments of romantic partners' negative moods, and examine the effects of gender and relational conflict on these pathways. Our studies revealed that both pathways consistently contributed to accuracy. Additionally, partners demonstrated greater indirect accuracy on conflict (vs. nonconflict) days, and indirect accuracy was somewhat higher for women than for men on conflict days (with the opposite pattern on nonconflict days). More importantly, we found evidence for a novel third pathway, in which the perception of conflict itself led to (correct) higher estimation of negative affect and thus, to higher accuracy. This pathway figured more consistently for men than for women. In our discussion, we link the pathways obtained in these studies to the extant social neuroscientific literature on empathy systems, arguing that the indirect pathway involves the effects of experience sharing, while the direct and conflict-based pathways involve the mental state attributions (Zaki & Ochsner, 2011). These findings demonstrate the importance of examining various empathic pathways for the understanding of empathic processes. (PsycINFO Database Record