The Dynamics of Intimate Relationships and their Dissolution during Young Adulthood

离婚是健康和幸福的关键决定因素，对几代人都有影响。 父母离异的孩子生活在贫困中的可能性是那些父母离异的孩子的两到三倍 已婚父母。但长久幸福的婚姻已经成为受过高等教育的人的特权；拥有四个孩子的夫妇-- 毕业一年的人结婚20年的可能性是高中学历的人的两倍(或 较少)教育。在过渡到成年的过程中，亲密关系建立了基础的关系技能， 年轻人在他们的生活中发扬光大。因此，了解哪些关系是持久的，又在 年轻的成年期对于我们理解成人离婚和子女的幸福至关重要。 我们提出的分析是通过新获得的关于亲密关系的密集纵向数据来实现的 18-22岁女性的关系--关系动态和社会生活(RDSL)项目。这个 RDSL数据使我们能够建立在先前研究的基础上，同时也克服了它的一些限制，包括 无法探索快速变化的关系特征作为后续预测因素的作用 解散。这些数据包括对亲密关系特征的每周随时间变化的测量，我们 将被用来预测随后的溶解。此外，由于短时间内经常接受采访， 回顾报道(大约一周)，我们可以包括非常短暂的亲密关系和/或 年轻人可能不记得(或选择忘记)的其他类型的亲密关系 被要求回忆他们过去几年的经历。 首先，我们建议估计离散时间Logit风险模型，这些模型每周最多利用165个 每名女性的访谈，以加深我们对从属/亲密关系、不平等决策、 冲突/亲密伴侣暴力、同时存在的性伴侣、伴侣的社会经济地位、共享/非共享 出生史和搅动(分手/和解)导致了解体的风险。我们会估计 每段关系第一次分手的时间模型，以及随后估计更高顺序的模型 言归于好的关系破裂。我们将在我们的多级危险中加入随机效应。 模型来解释个人在一段迅速融化的关系中的潜在倾向， 以说明数据的嵌套结构(数据包括每个妇女大约三个关系)。 其次，我们建议检查这些模式中的种族和社会经济(SES)差异。vbl.使用 图表技术、形式中介的测试和Oaxaca-Blinder分解模型，我们将探索 在总体关系解决率上是否存在种族和SES差异，如果存在，原因是什么？ 这是理想的数据集(RDSL)、团队(Barber、Kusunoki、Gatny)和方法(Logistic回归 风险模型、分解、中介分析)来回答这些研究问题，这些问题已经被 由于数据限制，未开发。

Divorce is a crucial determinant of health and well-being, with implications for multiple generations – children of divorced parents are between two and three times as likely to live in poverty compared to those with married parents. But a long, happy marriage has become a privilege of the highly educated; couples with a four- year degree are twice as likely to have a marriage that lasts 20+ years compared to those with a high school (or less) education. Intimate relationships during the transition to adulthood build foundational relation skills that young people carry forward in their lives. Thus, understanding which relationships endure and dissolve during young adulthood is crucial for our understanding of adult divorce and the well-being of children. We propose analyses that are made possible by newly available intensive longitudinal data on intimate relationships among 18 – 22 year old women – the Relationship Dynamics and Social Life (RDSL) project. The RDSL data allows us to build on prior research, while also overcoming some of its limitations, which included being unable to explore the role of rapidly changing relationship characteristics as predictors of subsequent dissolution. The data includes weekly time-varying measures of intimate relationship characteristics, which we will use to predict subsequent dissolution. Further, because of the frequent interviews with a short period of retrospective reporting (approximately one week), we can include very brief intimate relationships and/or other types of intimate relationships that young people might not remember (or choose to forget) when they are asked to recall their experiences over the past several years. First, we propose to estimate discrete-time logit hazard models that take advantage of up to 165 weekly interviews per woman, to further our understanding of how affiliation/intimacy, unequal decision-making, conflict/intimate partner violence, concurrent sexual partners, partner socioeconomic status, shared/unshared birth history, and churning (break-ups/reconciliations) contribute to the risk of dissolution. We will estimate models of the time to first break-up for each relationship, and subsequently estimate models for higher-order break-ups among relationships that reconciled. We will incorporate random effects into our multilevel hazard models to account for an individual’s underlying propensity to be in a relationship that dissolves quickly, and to account for the nested structure of the data (the data include approximately three relationships per woman). Second, we propose to examine race and socioeconomic (SES) differences in these patterns. Using graphing techniques, tests of formal mediation, and Oaxaca-Blinder decomposition models, we will explore whether there are race and SES differences in overall rates of relationship dissolution, and if so, why. This is the ideal dataset (RDSL), team (Barber, Kusunoki, Gatny), and approach (logistic regression hazard models, decomposition, mediation analyses) to answer these research questions, which have been unexplored due to data limitations.