首页|A validation of R-indicators as a measure of the risk of bias using data from a non-response follow-up survey
A validation of R-indicators as a measure of the risk of bias using data from a non-response follow-up survey
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NSTL
In many surveys, it has been noticed that non-response rates are on the increase and it is necessary to understand the impact on the estimation of population parameters, or the non-response bias. The covariance between variables that affect the probability of responding and a given survey variable is of interest to researchers. Since the non-response bias is variable dependent, detecting its presence and assessing its impact are major challenges to researchers. To overcome this challenge, many indicators are proposed for the assessment of non-response bias. The most popular among them are the representativity indicator (R-indicator) along with the partial R-indicator. The R-indicator offers an approach for summarizing the extent to which the respondents in a probability-based sample survey represent all sample units selected and their non-response bias. Partial R-indicators are used to develop survey designs to identify specific subgroups for targeted interventions. The usefulness of R-indicators depends on the availability of auxiliary data for their estimation, which may not always be availabe. The auxiliary data can be used for adjusting the non-response bias. The effect of such adjustments on R-indicators is also a matter of interest. This study investigates the potential limitations of R-indicators for a case study from the Swiss European Social Survey, which in round five, included a non-response followup survey.
NSTL
