Date of this Version
Published as Chapter 2 in Improving Surveys with Paradata: Analytic Uses of Process Information, edited by Frauke Kreuter (Hoboken, NJ: Wiley, 2013), pp. 13–42.
Nonresponse is a ubiquitous feature of almost all surveys, no matter which mode is used for data collection (Dillman et al., 2002) whether the sample units are households or establishments (Willimack et al., 2002) or whether the survey is mandatory or not (Navarro et al., 2012). Nonresponse leads to loss in efficiency and increases in survey costs if a target sample size of respondents is needed. Nonresponse can also lead to bias in the resulting estimates if the mechanism that leads to nonresponse is related to the survey variables (Groves, 2006). Confronted with this fact, survey researchers search for strategies to reduce nonresponse rates and to reduce nonresponse bias or at least to assess the magnitude of any nonresponse bias in the resulting data. Paradata can be used to support all of these tasks, either prior to the data collection to develop best strategies based on past experiences, during data collection using paradata from the ongoing process, or post hoc when empirically examining the risk of nonresponse bias in survey estimates or when developing weights or other forms of nonresponse adjustment. This chapter will start with a description of the different sources of paradata relevant for nonresponse error investigation, followed by a discussion about the use of paradata to improve data collection efficiency, examples of the use of paradata for nonresponse bias assessment and reduction, and some data management issues that arise when working with paradata.