A Next Generation Data Infrastructure to Understand Disparities across the Life Course

总结 这一基础设施提案旨在进一步提高社会科学（广义）的能力， 收集美国家庭和个人日常生活的数据。这些数据将更准确，更精细， 而且比传统的基于调查的研究更全面。这是因为 理解美国研究（UAS），我们一直在南加州大学建立的基于概率的互联网小组 自2014年起基础架构包括许多数据类型（包括调查数据、信息 从可穿戴设备、情境和管理联系、生态瞬时评估、自我记录 财务交易的电子记录），以及与更广泛的 在数据传播和征求对内容和方法的投入方面，研究界都发挥了重要作用。 UAS目前包括大约10，000名美国居民（包括加州的2，500人过采样）， 通过基于地址的抽样招募，并在需要时提供可上网的平板电脑。调查是 以英语和西班牙语进行。我们建议将UAS国家样本扩大到20，000名受访者， 黑人，亚洲人和西班牙裔人的子样本，每个至少3,000人。 收集的信息将侧重于解释一生中种族和社会经济差异的因素。 当然，包括种族歧视，获得教育和医疗保健的不平等， 物理和社会环境，以及更一般地说，一个人遇到的各种机会和障碍 在生命历程中。 拟议的项目小组涵盖了关于不平等、种族歧视、仇外心理和相关问题的广泛的实质性专门知识。 歧视、环境健康、儿童发展、健康和生命过程中的认知，以及 老龄化和退休、消费、工作和收入。为了鼓励研究界的投入，我们 设想一个由外部研究人员提出的关于不平等相关主题的补充调查模块方案， 作为年度研究会议。 该项目的基本前提是，全面调查的唯一可行办法是 生命历程的差异对观察到的不平等的影响是制定一个强大的多方法方法 它提供了尽可能多的解释因素的方法。在这种情况下，自然 可以利用发生的实验来帮助识别感兴趣的因果途径（例如，当空气质量在 由于工厂关闭，社区得到了改善;政策变化影响了某个地区学校的质量 地区）。因此，我们提出的项目旨在创建并向研究界提供一个独特的 详细和有针对性地收集有关美国家庭和个人的信息。为了实现这一目标，我们 将选定的外部数据源与无人机系统调查数据进行战略性整合，并扩展已经广泛的数据集 UAS调查涵盖的主题。

Summary This infrastructure proposal aims to further advance capabilities in the social sciences (broadly defined) to collect data on the daily lives of U.S. families and individuals. These data will be more accurate, more granular, and more comprehensive than is currently possible in traditional survey-based research. The context for this is the Understanding America Study (UAS), the probability-based Internet panel we have been building at USC since 2014. The infrastructure includes the combination of many data types (including survey data, information from wearables, contextual and administrative linkages, ecological momentary assessments, self-recorded narratives, and electronic records of financial transactions), as well as an open communication with the wider research community both in data dissemination and in soliciting input on content and methods. The UAS currently comprises about 10,000 U.S. residents (including a 2,500 person California oversample), recruited by address-based sampling and provided with Internet-enabled tablets if needed. Surveys are conducted in English and Spanish. We propose to expand the UAS national sample to 20,000 respondents, with subsamples of Blacks, Asians, and Hispanics of at least 3,000 each. The information collected will focus on factors explaining racial and socio-economic disparities over the life course, including racial discrimination, inequalities in access to education and healthcare, differences in the physical and social environment, and, more generally, the various opportunities and obstacles one encounters over the life course. The proposed project team covers a broad spectrum of substantive expertise on disparities, racial discrimination, environmental health, child development, health, and cognition over the life course, as well as aging and retirement, consumption, work, and income. To encourage input from the research community, we envision a program of added survey modules on inequality-related topics proposed by outside researchers, as well as an annual research conference. The basic premise underlying the project is that the only feasible approach to comprehensively investigating the impact of differences in the life course on observed inequality is to enact a robust multi-method approach that provides the means of accounting for as many explanatory factors as possible. Within that context, naturally occurring experiments can be exploited to help identify causal pathways of interest (e.g., when air quality in a neighborhood improves due to a plant closure; a policy change that affects the quality of schools in a certain area). Thus, our proposed project aims to create and make available to the research community a uniquely detailed and focused collection of information on U.S. households and individuals. To achieve this goal, we aim to strategically integrate selected external data sources with UAS survey data and expand the already broad set of topics covered by UAS surveys.