The development of large sample surveys creates new opportunities for analysis of subpopulations that would hitherto have been impossible to examine systematically. But it also raises key challenges. Low level measurement error can potentially lead to substantial biases in estimates drawn from small subsamples. This study details strategies researchers may take to make inferences in the context of this subsample-response-error problem. In the non-citizen voting case, which recently has received substantial attention, we show that attention to any of these strategies -- group-specific response error estimates, correlated higher-frequency events, or test-retest validity – produces significant evidence that non-citizens participated in recent US elections. Additional hypotheses that follow from the measurement error assumption are also not supported. We identify future steps to improve the reliability of estimates through in-survey test-retest in order to facilitate accurate sub-population identification for analyses.
Original Publication Citation
Richman, J., Earnest, D. C., & Chattha, G. (2016). Learning from small subsamples without cherry picking: The case of non-citizen registration and voting. [Working paper]. https://fs. wp. odu. edu/jrichman/wpcontent/uploads/sites/760/2015/11/AnsolabehereResponse10-19-2016. pdf.
Richman, Jesse; Earnest, David C.; and Chattha, Gulshan, "Learning from Small Subsamples without Cherry Picking: The Case of Non-Citizen Registration and Voting" (2016). Political Science & Geography Faculty Publications. 47.