Targeting on Social Networking Sites as Sampling Strategy for Online Migrant Surveys: The Challenge of Biases and Search for Possible Solutions
Choosing a methodology for migrant surveys usually is a complicated issue for a number of reasons, including the lack of information about sampling frames, and migrants’ status as a hard-to-reach population. The spread of social media usage among migrants has led researchers to look at the potential that Social Networking Sites (SNS) have for migration studies with respect to extracting and analyzing big data, conducting ethnography online, and reaching migrant respondents through SNS advertising. While the advantages of sampling migrants using SNS and surveying them online are clear, the drawbacks of this method—and, even more so, the potential solutions—constitute an almost unexplored field. In this chapter, we address one of the most significant challenges of using this strategy by exploring the biases it may present and the possible ways to resolve them. We use data from five SNS-based migrant surveys conducted during 2016–2018 with various groups of migrants and their adult children (second generation migrants) from Central Asian and Transcaucasian countries in Russia (with N varying from 302 to 12,524). After describing the procedure of surveying migrants with targeting on SNS, we outline the major biases, delineate possible solutions, and demonstrate how some of them—namely weighting based on dropout analysis and external validation—can work regarding the material from one of the surveys. We conclude that, at present, the range of biases remains more considerable than our opportunities to adjust for them, and so it may be time to concede this, and instead direct research efforts to exploring other approaches to data analysis and presentation that are more suitable for contexts of uncertainty—for example, fuzzy set theory and Bayesian statistics. This chapter contributes to the advancement of the emerging field of “tech-savvy” migration studies while signposting its bottlenecks and gains, as well as laying out directions for future research.