Using Propensity Scores for Nonresponse Adjustment with Covariate Measurement Error

Research question/goal: 

The proposed project will advance knowledge about the use of propensity scores for nonresponse adjustment when measurement error is present in the covariates used for adjustment. In particular, this project will (1) demonstrate, via simulations, the consequences of covariate measurement error for nonresponse adjustments as they are currently performed, (2) investigate the amount and structure of measurement error present in readily available auxiliary variables and paradata collected through interviewers, (3) examine the effect of known differential measurement error on nonresponse adjustment, and (4) develop new methods to perform propensity score nonresponse adjustments in the presence of covariate measurement error. Addressing the issue of measurement errors in nonresponse adjustment variables will affect population estimates of key statistics spanning a wide range of topics, such as welfare recipiency, reproductive behaviour, and health.  Our goal is to understand the amount and consequences of these errors and to propose practical steps for addressing them.  This work will also push propensity score methods more generally in important new directions, in particular by assessing the effects of measurement error on the performance of propensity score approaches, and by developing methods to handle differentially measured covariates.

Current stage: 

The project broadened its scope to include nonresponse adjustment problems for nonprobability surveys. To this end, we organized a session at the Annual Meeting of the American Association for the Advancements of Sciences (AAAS) in 2018 and published an invited piece for the Annual Review of Statistics and Its Applications. We expect to submit a research proposal to the German Research Foundation (DFG) in the course of 2019.

Fact sheet

2015 to 2019
in preparation