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使用微观数据系列(IPUMS)进行分析时对方差和可信区间的估计。综合人口普查系统以统一的格式向学者免费提供1850年至2000年十年一次的人口普查微观数据，使用方便的数据查询系统，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，产生了大约1000本书、文章、论文和工作论文。本补编将支持“IPUMS重新设计”(HD43392)，其目的是改进和更新IPUMS数据、文件和访问工具。大多数使用IPUMS的研究人员使用不适当的方差估计方法；他们几乎普遍使用针对简单随机样本设计的方法。然而，由于聚类、分层和权重调整，这种方法产生不准确的p值和可信区间。错误的方差估计会导致错误的研究结论和政策建议。我们计划开展三项具体的研究活动，以简化基于IPUMS的研究中可靠的方差估计的构建：(1)构建和记录为所有美国人口普查微数据开发方差估计软件所需的新变量。这些变量包括1960年至2000年期间的分层变量，以及较早人口普查的群集化和次抽样重复变量。(2)通过对使用泰勒级数线性化、次抽样重复估计和人口普查局设计因素的分析的比较，使用真实世界的应用程序来评估人口普查中方差估计的可靠性。(3)根据这次评估的结果，使用每个统计程序包编制和传播便于用户使用的方差估计文件和建议，并提供典型分析和样本程序的例子。

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.