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: 

A paper entitled ‘Theory and Practice in Nonprobability Surveys: Parallels Between Causal Inference and Survey Inference’, prepared within the scope of the proposal submission and co-authored with colleagues at Pew Research Center, has been published in Public Opinion Quarterly. Subsequent simulation studies are still in progress. Data collection, conducted in collaboration with the German Institute for Employment Research and the U.S. Census Bureau, is ongoing. The objective is to obtain sufficient data to eventually allow for quantifying differential measurement error. Results from the preliminary study have been accepted for publication and are in print. In May 2017, a proposal for a spin-off project to capture and further investigate the measurement error was submitted to the DFG. The decision is expected shortly.

Fact sheet

2015 to 2018
in preparation