Evaluating, Comparing, Monitoring, and Improving Representativeness of Survey Response Through R‐Indicators and Partial R‐Indicators

Summary 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 subpopulationsthatneedincreasedeffort.Inthispaper,wepresentanoverviewofrepresentativeness indicators or R-indicators that are fit for these purposes. We give several examples and provide guidelines for their use in practice.