| Literature DB >> 27212786 |
Raphael Nishimura1, James Wagner2, Michael R Elliott3.
Abstract
The growth of nonresponse rates for social science surveys has led to increased concern about the risk of nonresponse bias. Unfortunately, the nonresponse rate is a poor indicator of when nonresponse bias is likely to occur. We consider in this paper a set of alternative indicators. A large-scale simulation study is used to explore how each of these indicators performs in a variety of circumstances. Although, as expected, none of the indicators fully depicts the impact of nonresponse in survey esti mates, we discuss how they can be used when creating a plausible account of the risks for nonresponse bias for a survey. We also describe an interesting characteristic of the FMI that may be helpful in diagnosing NMAR mechanisms in certain situations.Entities:
Keywords: Bias; Missing data; Nonresponse; Nonresponse indicators; Survey data quality measures
Year: 2015 PMID: 27212786 PMCID: PMC4871316 DOI: 10.1111/insr.12100
Source DB: PubMed Journal: Int Stat Rev ISSN: 0306-7734 Impact factor: 2.217