Reduction of Nonresponse Bias Through Case Prioritization
Peytchev, A. A., Riley, S., Rosen, J. A., Murphy, J., & Lindblad, M. (2009, May). Reduction of Nonresponse Bias Through Case Prioritization. Presented at AAPOR 2009, .
Improving response rates to minimize nonresponse bias is often a key goal in survey research. How response rates are increased can determine the remaining nonresponse bias in estimates. Studies often target sample members that are most likely to be interviewed to maximize response rates. Yet the now widely accepted stochastic model for survey participation would suggest that this may not reduce nonresponse bias, further supported in the empirical literature. Counter to current practice, we suggest targeting of likely nonrespondents from the onset of the study with a different protocol, in order to minimize nonresponse bias. To achieve such targeting, various sources of information can be incorporated: paradata collected by the interviewers, demographic and substantive survey data from prior waves, and administrative data. Using these data, sample cases are identified on which a more effective, often more costly, survey protocol can be employed to gain respondent cooperation.
This paper describes the two components of this approach to reducing nonresponse bias, demonstrates methods used to create case priority assignments based on response propensity models, and presents empirical results from the use of a different protocol for prioritized cases. In a field data collection, a random half of cases with low response propensity received higher priority and increased resources to assure their completion. Resources for high-priority cases were allocated as interviewer incentives; random assignment was made within interviewer workload. The other half of these low response propensity cases served as the control group.