Missing data due to non-response impose a serious threat to the quality of surveys and register-based statistics. Non-response is often not a random phenomenon . It usually depends on demographic and socio-economic characteristics of individuals or enterprises. Also the data collection process may have a sun=bstantial influence.
The response rate is often used as an indicator for survey quality. It has the advantage that is can be easily computed. However, low response rates will not necessarily cause estimates to be biased. There are ample examples in the literature where increased data collection efforts has lead to a higher response rate but also to a larger non-response bias.
To assess the effects of nonresponse on the quality of statistics, other quality indicators are needed. These indicators should measure the degree to which respondents and non-respondents differ from each other. In other words, such indicators should measure the degree to which the group of respondents in a survey or register resembles the population. The indicators are called Representativity Indicators or, for short, R-indicators.
It is the objective of the RISQ Project to develop R-indicators, to explore their characteristics and to show how to implement and use them in a practical data collection environment.
The project will demonstrate that R-indicators are not only very useful in the analysis of survey data, but also during fieldwork. They can be used to the monitor data collection processes, and therefore facilitate efficient allocation of interviewing resources.
More about R-indicators...