Using paradata to assess and improve survey data quality 1

Convenor: Dr Caroline Vandenplas
Affiliation: KULeuven
Email: carolinevandenplas83@gmail.com

Session Details

Survey methodologists are currently facing challenges of declining response rates, increasing risk of nonresponse bias and measurement error, as well as escalating costs of survey data collection. An approach, with limited costs, to tackle these challenges is the use of paradata. Paradata, data about the survey process, have always been present but the range and detail level of them have considerably increased with the computerization of the data collection process. Such data can be used to detect and eventually reduce systematic survey errors and increase data quality, during the fieldwork (adaptive designs) or in post-survey adjustment. Paradata can also be used to reduce the cost of the survey process as it is done to determine caps on the number of phone call attempts in telephone surveys.
We are interested in papers that apply the use of paradata to detect and improve data quality or/and reduce survey costs. For instance, time and timing are both linked to the survey costs and the data quality, two essential elements of a survey. The timing of the visits, calls or sent-out of questionnaire/request and reminders has been shown to be determining for survey participation. At the same time, requesting that interviewers work in the evening or at the weekend or making sure that the reminders to a Web or mail surveys are sent timely may have cost implications. Nonresponse error is not the only type of survey error to be linked to time: the time taken to answer a question, also called response latency, is known to echo the cognitive effort of the respondent and, hence, data quality. On the other hand, the interviewer speed can also influence data quality. Moreover the interviewer speed has been shown to be dependent of the rank of the interview.

The aim of this session is to reflect on possible links between paradata reflecting ‘easy’ measured characteristic of different steps of the survey process and data quality. Such a link could then help data collection manager and researcher to detect potential systematic survey errors in a fieldwork monitoring or post-evaluation context and lead to opportunities to prevent or correct for these errors. We invite papers demonstrating a link between paradata and data quality as well as papers showing how this link can be used to increase data quality or reduce cost.