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Mixed mode data collection designs are increasingly being adopted with the hope that they may  reduce  selection  errors  in  single  mode  survey  designs.  Yet  possible  reductions  in selection  errors  achieved  by  mixing  modes  may  be  offset  by  a  potential  increase  in  total survey error due to extra measurement error being introduced by the additional mode(s). Few studies  have  investigated  this  empirically,  however.  In  the  present  study,  we  compute  the Mean  Squared  Error  (MSE)  for  a  range  of  estimates  using  data  from  a  mode  comparison experiment. We compare two mixed mode designs (a sequential web plus mail survey, and acombined concurrent and sequential CATI plus mail survey) with a single mode mail survey.The  availability  of  auxiliary  data  on  the  sampling  frame  allows  us  to  estimate  several components of MSE (sampling variance, non-coverage, nonresponse and measurement bias) for  a  number  of  sociodemographic  and target  variables.  Overall,  MSEs  are  lowest  for  thesingle mode survey, and highest for the CATI plus mail design, though this pattern is not consistent across all estimates. Mixing modes generally reduces total bias, but the relative contribution to total survey error from different sources varies by design and by variable type.status: publishe

Estimating Components of Mean Squared Error to Evaluate the Benefits of Mixing Data Collection Modes