The Dynamics of Intimate Relationships and their Dissolution during Young Adulthood

离婚是健康和福祉的关键决定因素，对多代人都有影响—— 离婚父母的孩子生活在贫困中的可能性是父母离婚的孩子的两到三倍。 已婚父母。但长久、幸福的婚姻已成为受过高等教育的人的特权。有四人的夫妇 拥有高中学历的人拥有持续 20 年婚姻的可能性是拥有高中学历（或 少）教育。在向成年过渡期间的亲密关系建立了基本的关系技能， 年轻人在生活中不断前进。因此，了解哪些关系在这段时间内持续存在和消失 成年早期对于我们理解成人离婚和儿童福祉至关重要。 我们提出的分析可以通过新获得的关于亲密关系的密集纵向数据来实现。 18 – 22 岁女性之间的关系 – 关系动态和社交生活 (RDSL) 项目。的 RDSL 数据使我们能够以先前的研究为基础，同时克服其一些局限性，其中包括 无法探索快速变化的关系特征作为后续关系的预测因素的作用 溶解。这些数据包括每周随时间变化的亲密关系特征测量值，我们 将用于预测随后的溶解。此外，由于短期内频繁采访 回顾性报告（大约一周），我们可以包括非常简短的亲密关系和/或 年轻人可能不记得（或选择忘记）的其他类型的亲密关系 被要求回忆过去几年的经历。 首先，我们建议估计离散时间 Logit 风险模型，该模型利用最多 165 周 对每位女性进行访谈，以进一步了解归属感/亲密关系、不平等的决策、 冲突/亲密伴侣暴力、同时性伴侣、伴侣社会经济地位、共享/非共享 出生史和搅动（分手/和解）会增加解散的风险。我们将估计 每个关系第一次分手时间的模型，然后估计高阶模型 和解的关系之间的破裂。我们将把随机效应纳入我们的多级危险中 模型可以解释个人处于快速消失的关系中的潜在倾向，以及 解释数据的嵌套结构（数据包括每个女性大约三个关系）。 其次，我们建议研究这些模式中的种族和社会经济（SES）差异。使用 我们将探索绘图技术、形式中介测试和 Oaxaca-Blinder 分解模型 关系解除的总体比率是否存在种族和社会经济地位差异，如果有，原因是什么。 这是理想的数据集（RDSL）、团队（Barber、Kusunoki、Gatny）和方法（逻辑回归） 危害模型、分解、中介分析）来回答这些研究问题 由于数据限制尚未探索。

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.