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

Objective: Therapists’ empathic accuracy (EA) toward their clients’ fluctuating emotions is a crucial clinical skill that underlies many therapeutic interventions. In contrast to the subjective components of empathy, limited empirical work has addressed EA or its effect on the outcomes of psychotherapy. Here, we differentiate between the components of EA (tracking accuracy, directional discrepancy) as well as the valence of the target emotions (positive vs. negative). We also investigated the relative contribution of cognitive and emotional processes to therapists’ EA and examined the associations between EA and treatment outcomes. Method: The sample comprised 93 clients treated by 62 therapists in a university setting. Prior to each session, clients self-reported their symptoms. Following each session, clients rated their positive (PE) and negative (NE) emotions during the session and therapists rated their own emotions, as well as their assessment of their clients’ emotions. Results: Therapists accurately tracked their clients’ PE and NE and were more accurate for NE. Therapists tended to overestimate their clients’ NE and underestimate their clients’ PE. Therapists’ emotions were associated with their clients’ emotions (real similarity). Therapists’ emotions were also associated with their assessments of their clients’ emotions (assumed similarity). Therapists’ own emotions partially mediated the association between clients’ emotions and therapists’ assessments. Therapists’ inaccuracy in assessing their clients’ PE was associated with higher reported symptoms in the next session. Conclusion: These findings help provide a better understanding of the specific characteristics associated with more EA and underscore the importance of EA in facilitating clients’ emotional well-being.

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 developments in personality research highlight the value of modelling dynamic state-like manifestations of personality. The present work integrates these developments with prominent clinical models addressing within-person multiplicity and promotes the exploration of models centred on state-like manifestations of personality that function as cohesive organizational units. Such units possess distinct subjective qualities and are characterized by specific affects, behaviours, cognitions, and desires that tend to be co-activated. As background, we review both theory and research from the fields of social cognition, psychotherapy, and psychopathology that serve as the foundation for such models. We then illustrate our ideas in greater detail with one well-supported clinical model-the schema therapy mode model, chosen because it provides a finite and definite set of modes (i.e. cohesive personality states). We assessed these modes using a newly developed experience-sampling measure administered to 52 individuals (four times daily for 15 days). We estimated intraindividual and group-level temporal and contemporaneous networks based on the within-person variance as well as between-person network. We discuss findings from exemplar participants and from group-level networks and address cross-model particularities and consistencies. In conclusion, we consider potential idiographic and nomothetic applications of subjective states dynamic personality research based on intensive longitudinal methods.