Potential Undercoverage and Bias in Name-based Samples of Foreigners
Rainer Schnell, University of Duisburg-Essen. Institute of Sociology
Keywords: sampling, special populations, coverage bias, name based classification
In many cases there are no sampling frames for rare or special populations like foreigners or migrants. Therefore, often sampling frames for a more general population are screened for members of the target population. Whereas this is an efficient way of sampling rather rare and specific populations, knowledge of potential limitations and threats of this widely used approach is very limited.
Screening and classifying members of a population according to some criteria may produce false positive matches (e.g. natives wrongly classified as foreigners) as well as false negatives (e.g. foreigners wrongly classified as domestic). Whereas false positives only increase screening or survey costs, false negatives potentially introduce bias if they are systematically different in variables relevant to the topic of the survey.
Name-based sampling has been applied to different nationalities and groups of migrants. It also has shown to be an efficient method for sampling of turkish migrants (and their descendants) which present the largest group of migrants in Germany (e.g. Razum et al. 2001). We use a large scale German panel survey ('PASS', on labour market and social security) to investigate coverage problems and potential biases when using a Bayesian-based classification of names to screen for foreigners in a general population sampling frame. We will present results on biased estimates of migration and labour force variables, introduced by false negative name classifications.
References: Razum, Oliver; Zeeb, Hajo & Akgün, Seval, 2001: How Useful is a Name-based Algorithm in Health Research Among Turkish Migrants in Germany. Tropical Medicine and International Health 6(8): 654-661.