Evaluating, Comparing, Monitoring, and Improving Representativeness of Survey Response Through R-Indicators and Partial R-Indicators
Evaluating, Comparing, Monitoring, and Improving Representativeness of Survey Response Through R-Indicators and Partial R-Indicators
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通过 R 指标和部分 R 指标评估、比较、监测和提高调查响应的代表性
DOI：
10.1111/j.1751-5823.2012.00189.x
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发表时间：
2012
影响因子：
2
通讯作者：
Schouten B
中科院分区：
文献类型：
--
作者：
Schouten B
在许多调查中，无答复是常见的误差来源。由于调查往往是昂贵的工具，质量和成本的权衡在调查的设计和分析中发挥着持续的作用。电话、计算机和互联网的进步都对调查的设计产生了相当大的影响。最近，对调查数据收集、监测和调整方法的强烈关注已成为有效减少无应答误差的新范例。ParaData和适应性调查设计是这些新发展的关键词。评价、比较、监测和提高调查答复质量的先决条件是具有代表性的调查答复的概念框架、衡量其偏差的指标以及确定需要增加努力的次级群体的指标。在本文中，我们概述了适合于这些目的的代表性指标或指标。我们给出了几个例子，并提供了它们在实践中的使用指南。
Non‐response is a common source of error in many surveys. Because surveys often are costly instruments, quality‐cost trade‐offs play a continuing role in the design and analysis of surveys. The advances of telephone, computers, and Internet all had and still have considerable impact on the design of surveys. Recently, a strong focus on methods for survey data collection monitoring and tailoring has emerged as a new paradigm to efficiently reduce non‐response error. Paradata and adaptive survey designs are key words in these new developments. Prerequisites to evaluating, comparing, monitoring, and improving quality of survey response are a conceptual framework for representative survey response, indicators to measure deviations thereof, and indicators to identify subpopulations that need increased effort. In this paper, we present an overview of representativeness indicators orR‐indicators that are fit for these purposes. We give several examples and provide guidelines for their use in practice.