Comparing Measures of Survey Data Quality

描述(由申请人提供)：无回答偏差的风险是健康调查有效性的主要威胁。许多专门研究发现，这种偏见会影响重要健康措施的结果。不幸的是，关于如何评估无反应偏差的风险，几乎没有指导意见。许多调查依赖回复率作为关键统计数据。然而，最近的一项荟萃分析表明，应答率不是反映无反应偏见的一个很差的指标。在所调查的研究中，应答率和无应答偏差之间几乎没有相关性。缺乏一个衡量偏见风险的良好指标在两个方面对健康调查有害。首先，回应率高、无回复率偏高的调查可能被认为是有效的并被公布。其次，回复率低、无回复率偏低的调查可能会受到质疑，并被拒绝发表。这两种情况都可能导致关于卫生政策和做法的错误结论。本提案试图通过评估一套关于无反应偏差风险的指标来填补这一空白。这些衡量标准(包括应答率)中的每一个都作出了在大多数实际情况下无法检验的假设。这项研究的目的是了解几种替代措施的优点和缺点，以及不正确假设的影响。通过对这些指标的关键属性的推导，对它们进行了比较和对比。这些属性可以包括对如何使用每种测量方法来限制潜在的无响应偏差以及这样做所需的假设的描述。此外，还将进行一项模拟研究，以演示每项措施在不同条件下的表现。最后，所有措施都将适用于现有的调查数据收集。拟议项目的目标是帮助就一套可用来评估无反应偏差风险的措施达成共识。这应极大地促进评价与健康有关的调查数据质量的努力。 公共卫生相关性：无回答偏差的风险是健康调查有效性的主要威胁。许多专门研究发现，这种偏见会影响重要健康措施的结果。不幸的是，关于如何评估无反应偏倚的风险，几乎没有指导意见。缺乏合理的、被普遍接受的偏差风险指标在两个方面对健康研究有害。首先，回应率高、无回复率偏高的调查可能被认为是有效的并被公布。通过这种方式，可以传播有偏见的结果。其次，回复率低、无回复率偏低的调查可能会受到质疑，并被拒绝发表。这两种情况都可能导致关于卫生政策和做法的错误结论。本提案试图通过评估一套替代指标来填补这一空白，以衡量无反应偏差的风险。该评估包括对其特性的理论推导、模拟研究以及对现有测量数据集合的应用。

DESCRIPTION (provided by applicant): The risk of non response bias is a major threat to the validity of health surveys. Many special studies have found that this bias can impact the results of important health measures. Unfortunately, there is very little guidance about how to evaluate the risk of nonresponse bias. Many surveys rely on the response rate as a key statistic. However, a recent meta-analysis indicates that the response rate is a poor indicator for non response bias. In the studies examined, there was little or no correlation between the response rate and the non response bias. The lack of a good indicator for the risk of bias is harmful to health surveys in two ways. First, surveys with high response rates and relatively high non response bias may be accepted as valid and published. Second, surveys with low response rates and relatively low non response bias may be questioned and rejected for publication. Both of these situations may lead to incorrect conclusions about health policies and practices. The present proposal attempts to fill this void by evaluating a set of indicators for the risk of non response bias. Each of these measures (including the response rate) makes assumptions that are untestable in most practical situations. The goal of this research is to understand the strengths and weaknesses of each of several alternative measures and the implications of incorrect assumptions. These indicators are compared and contrasted through derivation of their key properties. These properties can include a description of how each measure can be used to place bounds on the potential non response bias and the assumptions required to do so. In addition, a simulation study will be conducted to demonstrate how each measure performs under a varied set of conditions. Finally, all the measures will be applied to existing survey data collections. The goal of the proposed project is to aid in the development of a common understanding of a set of measures that can be used to evaluate the risk of non response bias. This should greatly facilitate efforts to evaluate the quality of health-related survey data. PUBLIC HEALTH RELEVANCE: The risk of non response bias is a major threat to the validity of health surveys. Many special studies have found that this bias can impact the results of important health measures. Unfortunately, there is very little guidance regarding how to evaluate the risk of non response bias. The lack of reasonable, commonly-accepted indicators for the risk of bias is harmful to health research in two ways. First, surveys with high response rates and relatively high non response bias may be accepted as valid and published. In this way, biased results may be propagated. Second, surveys with low response rates and relatively low non response bias may be questioned and rejected for publication. Both of these situations may lead to incorrect conclusions about health policies and practices. The present proposal attempts to fill this void by evaluating an alternative set of indicators for the risk of non response bias. This evaluation includes theoretical derivation of their properties, a simulation study, and application to existing survey data collections.