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

Adult attachment and the perception of emotional expressions: Probing the hyperactivating strategies underlying anxious attachment

ABSTRACT According to adult attachment theory, individual differences in attachment-related anxiety reflect variation in individuals' vigilance to cues relevant to appraising and monitoring the availability and responsiveness of significant others. To investigate this assumption, the authors adopted a morph movie paradigm in which participants were shown movies of faces in which an emotional facial expression changed gradually to a neutral one (Study 1) or a neutral expression changed to an emotional one (Studies 2-4). Participants were asked to judge the point at which the emotional expression had disappeared or emerged, respectively. Individuals who were highly anxious with respect to attachment were more likely to perceive the offset (Study 1) as well as the onset (Studies 2 and 3) of the facial expressions of emotion earlier than other people. Moreover, this heightened state of vigilance may have led to poore

Research design boundaries for qualitative research, stakeholder and patient and public involvement, and why they matter

Within current mainstream understandings of patient and public involvement (PPI) in health research, a clear distinction is made between what ‘involvement’ in research is: ‘research being carried out ‘with’ or ‘by’ members of the public rather than ‘to’, ‘about’ or ‘for’ them'1 and what it is not: namely ‘engagement with’ and ‘participation in’ research. Research evidence describes problems than can arise when such distinctions are unclear or misunderstood (often by those new or unfamiliar with PPI); or when distinctions are intentionally blurred e.g. by ‘dual roles’ being created within some projects, where research participants also advise on the conduct of projects. What is less widely examined, however, is the blurring of boundaries between the object of enquiry which is the business of PPI for that project, the data which is the object of qualitative collection involving discussion with participants and the purposeful research activities which are best progressed through engagement with stakeholders. This poster draws upon case study findings from two recent, similar National Institute for Health Research (NIHR)-funded evaluations of PPI in health research: RAPPORT (England-wide) and IMPRESS (regional research programme-specific), pertaining to how researchers (from various disciplines, using various research designs) within different case study research projects can blur the boundaries between qualitative research, stakeholder events and PPI. We pose questions pertaining to the consequences of blurred research design boundaries for the success of outcomes of public-research collaborations. We query why, and to what extent, such distinctions matter in co-producing knowledge and in measuring the impact of various investments in collaborative research activities

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