Auxiliary variables a weight against nonresponse bias : A simulation study

Abstract: Today’s surveys face a growing problem with increasing nonresponse.  The increase in nonresponse rate causes a need for better and more effective ways to reduce the nonresponse bias.  There are three major scientific orientation of today’s research dealing with nonresponse. One is examining the social factors, the second one studies different data collection methods and the third investigating the use of weights to adjust estimators for nonresponse.  We would like to contribute to the third orientation by evaluating estimators which use and adjust weights based on auxiliary variables to balance the survey nonresponse through simulations. For the simulation we use an artificial population consisting of 35455 participants from the Representativity Indicators for Survey Quality project. We model three nonresponse mechanisms (MCAR, MAR and MNAR) with three different coefficient of determination s between our study variable and the auxiliary variables and under three response rates resulting in 63 simulation scenarios. The scenarios are replicated 1000 times to acquire the results. We outline our findings and results for each estimator in all scenarios with the help of bias measures.