Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies
Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies
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DOI：
10.1073/pnas.1917036117
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发表时间：
2020-08-11
影响因子：
11.1
通讯作者：
Wolf, Scott
中科院分区：
文献类型：
--
作者：
Joel, Samantha;Eastwick, Paul W.;Wolf, Scott
考虑到关系质量对健康和幸福的巨大影响，关系科学的一个核心任务就是解释为什么一些浪漫关系比另一些更繁荣。这个大型项目使用机器学习（即随机森林）来1)量化关系质量可预测的程度，2)确定哪些结构可靠地预测关系质量。在来自29个实验室的43个二元纵向数据集中，关系质量的顶级关系特定预测因子是感知伴侣承诺、欣赏、性满意度、感知伴侣满意度和冲突。最主要的个体差异预测因子是生活满意度、负面影响、抑郁、依恋回避和依恋焦虑。总体而言，关系特异性变量在基线时预测高达45%的方差，在每个研究结束时预测高达18%的方差。个体差异也表现良好（分别为21%和12%）。行为者报告的变量（即，自己的特定关系和个体差异变量）预测的方差是伴侣报告变量（即，伴侣对这些变量的评级）的两到四倍。重要的是，个体差异和伴侣报告除了单独的行为者报告的关系特定变量外，没有预测作用。这些发现表明，所有个体差异和伴侣经历的总和通过一个人自己的特定关系经历对关系质量产生影响，并且由于个体差异和伴侣报告的调节作用而产生的影响可能很小。最后，关系质量的变化（即在研究过程中关系质量的增加或减少）在很大程度上是无法从自我报告变量的任何组合中预测的。这种集体努力应该指导未来的关系模式。
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 relationshipspecific 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.