IPUMS Redesign Supplement (RMI)

描述（由申请人提供）： 该提案寻求资金，以改进在使用综合公共用途微数据系列进行分析时对方差和置信区间的估计。人口普查研究所通过一个方便用户的数据访问系统，以统一格式向学者免费提供1850年至2000年期间十年一次的人口普查微观数据，并提供全面的文件。自1995年初步发布以来，IPUMS已成为世界上使用最广泛的人口资源之一，并产生了大约一千本书籍，文章，论文和工作文件。 该补充将支持“IPUMS重新设计”（HD 43392），旨在改进IPUMS数据、文档和访问工具并使其现代化。大多数使用IPUMS的研究人员应用不适当的方差估计方法;他们几乎普遍应用为简单随机样本设计的方法。然而，由于聚类、分层和加权调整，这些方法产生不准确的p值和置信区间。错误的方差估计会导致错误的研究结论和政策建议。我们计划三个具体的研究活动，以简化IPUMS为基础的研究可靠的方差估计的建设：（1）构建和文件的新变量，利用方差估计软件的所有美国人口普查微观数据。这些变量包括1960年至2000年期间的分层变量以及以前人口普查的聚类和子样本重复变量。(2)基于使用泰勒级数线性化、子样本重复估计和人口普查局设计因素的分析比较，使用真实世界的应用程序评估人口普查中方差估计的可靠性。(3)根据这一评价的结果，编制和传播便于用户使用的文件和建议，以便使用每一统计数据包进行方差估计，并提供典型分析和抽样程序的例子。

DESCRIPTION (provided by applicant): This proposal seeks funding to improve estimation of variances and confidence intervals in analyses using the Integrated Public Use Microdata Series (IPUMS). The IPUMS makes decennial census microdata for the period 1850 through 2000 freely available to scholars in harmonized format through a user-friendly data access system with comprehensive documentation. Since its preliminary release in 1995, the IPUMS has become one of the most widely used demographic resources in the world, and has generated approximately a thousand books, articles, dissertations, and working papers. This supplement will support "IPUMS Redesign" (HD43392), which aims to improve and modernize IPUMS data, documentation, and access tools. Most researchers using IPUMS apply inappropriate methods of variance estimation; they almost universally apply methods designed for simple random samples. Because of clustering, stratification, and weighting adjustments, however, such methods yield inaccurate p-values and confidence intervals. Bad variance estimates lead to erroneous research conclusions and policy recommendations. We plan three specific research activities to simplify the construction of reliable variance estimates in IPUMS-based research: (1) Construct and document new variables necessary to exploit variance estimation software for all U.S. census microdata. These include a stratification variable for the period from 1960 to 2000 and clustering and subsample-replicate variables for the earlier censuses. (2) Evaluate the reliability of variance estimates in the censuses using real-world applications, based on comparison of analyses that use Taylor series linearization, subsample-replicate estimates, and Census Bureau design factors. (3) Based on the results of this evaluation, develop and disseminate user-friendly documentation and recommendations for variance estimation using each statistical package, with examples of typical analyses and sample programs.