Estimating RDD Survey Bias Using ZIP Code Matching to Census Data

使用邮政编码与人口普查数据匹配来估计 RDD 调查偏差

• 批准号: 0818931
• 负责人: Paul Biemer
• 金额: $ 14.38万
• 依托单位: Research Triangle Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-15 至 2012-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0818931&HistoricalAwards=false
• 关键词: Estimating; RDD; Survey; Bias; Using

Low and declining response rates in household surveys provide the potential for nonresponse bias, but assessing the nonresponse bias is a challenging problem when characteristics of nonrespondents are unknown. For example, for random-digit-dial (RDD) surveys, information about nonrespondents may be limited to just the telephone number. To address this issue, the census geocoding (CG) method has sometimes been used. This method appends aggregate census information for small geographic units (for example, census blocks, block groups, or tracts) to nonresponding cases located in those units. These census data can then be used to evaluate nonresponse bias or adjust for it. Little is known about the effectiveness of the CG approach, but it is often used in nonresponse evaluations, particularly for RDD surveys. While the CG method is an important tool for recovering information from nonrespondents, it relies on the degree to which its assumptions are met. This research project will investigate the efficacy of the CG method using RDD and face-to-face survey data and will develop guidelines for implementing it so as to maximize its effectiveness for use in household surveys. One of the goals of the proposed project is to investigate the minimum amount of information required to apply the CG approach effectively. As an example, if only a telephone number is known, census aggregate data closer to the telephone exchange-level must be used rather than at the block-level. Finding a method that can be used effectively to evaluate and correct for nonresponse bias in household surveys will be an important contribution to survey research.Much of social science relies on probability-based surveys, which in turn rely on the ability to obtain survey measures from all sample members. Nonresponse threatens probability-based inference. This project will advance the knowledge about methods for evaluating and adjusting for nonresponse bias in surveys. The project will not only evaluate the CG method for use in telephone surveys but for virtually any survey where the CG method can be used. This includes any survey where information on nonrespondents is limited to an address, ZIP code, or telephone number and very little else. Many household surveys use address frames where all that is known for a frame unit is the household family name, the address and, in some cases, a telephone number. The CG approach can be applied in these situations to evaluate the bias due to nonresponse. The research is supported by the Methodology, Measurement, and Statistics Program and a consortium of federal statistical agencies as part of a joint activity to support research on survey and statistical methodology.

住户调查的答复率很低，而且不断下降，这可能造成无答复偏倚，但在不知道无答复者的特征时，评估无答复偏倚是一个具有挑战性的问题。 例如，对于随机数字拨号（RDD）调查，有关非受访者的信息可能仅限于电话号码。 为了解决这个问题，有时使用普查地理编码方法。 此方法将小地理单元（例如，人口普查区块、区块组或区域）的聚合人口普查信息附加到位于这些单元中的无响应案例。 这些普查数据，然后可以用来评估nonresponse偏差或调整it.Little的CG方法的有效性所知甚少，但它经常被用于nonresponse评估，特别是RDD调查。 虽然CG方法是从非受访者那里恢复信息的重要工具，但它依赖于其假设得到满足的程度。 该研究项目将利用关系数据库和面对面调查数据调查协商小组方法的效力，并将制定实施准则，以最大限度地发挥其在住户调查中的效力。 拟议项目的目标之一是调查有效应用CG方法所需的最低信息量。 例如，如果只知道一个电话号码，则必须使用更接近电话交换机一级而不是块一级的普查汇总数据。 找到一种可以有效地用于评估和纠正住户调查中的无回答偏差的方法将是对调查研究的重要贡献。社会科学的大部分依赖于基于概率的调查，而这反过来又依赖于从所有样本成员中获得调查措施的能力。 不回答威胁到基于概率的推理。 该项目将推进有关评估和调整调查中无回答偏差的方法的知识。 该项目不仅将评估CG方法在电话调查中的使用，而且将评估CG方法在几乎任何可以使用的调查中的使用。 这包括任何调查，其中非受访者的信息仅限于地址，邮政编码或电话号码，其他信息很少。 许多住户调查使用地址框，在这种情况下，一个框单位所知的只是家庭的姓氏、地址，在某些情况下还有电话号码。 在这些情况下，可以应用CG方法来评估无应答引起的偏倚。 这项研究得到了方法、测量和统计方案以及联邦统计机构联合会的支持，作为支持调查和统计方法研究的联合活动的一部分。