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This section focuses on the representation side of the Total Survey Error framework linking fieldwork practices to probability sampling designs. Previous research has investigated the impact of sampling error on the “representativeness” of a survey’s target population. While confidence in good coverage of the target population is crucial, correct random sampling is also a highly dependent on the survey interviewers and their supervisors, as they select the dwellings, households, and respondents in the field. Depending on the cultural context, the availability of solid sampling frames, field organisations’ general guidelines, and other aspects, fieldwork practice may largely influence a survey’s representativeness.
We invite papers studying the extent to which fieldwork practices and sampling designs affect representation error using existing survey data. We are also interested in papers discussing coverage issues of (special) populations and in approaches mitigating these during fieldwork, including applied solutions to monitor, reduce, and tackle fieldwork influences on sampling designs. In addition, we welcome applied and novel approaches to estimate the impact of fieldwork practices on random sampling, instruments that may circumvent this problem, such as further automated respondent selection or random walks with geo-fencing, and other techniques to control random selection at all stages. Finally, we aim to attract papers addressing how fieldwork practices affect random sampling more generally.

Potential topics could include, but are not limited to the following:

• How can we best deal with the lack of sampling frames?
• Under what circumstances should special populations or subgroups of a population (i.e. internally displaced persons) be excluded? When do we consider under-coverage?
• What can we do, when the best available sampling frame is not good enough? Is it appropriate to apply additional methods to ensure good coverage? How would these look like?
• What fieldwork practices are harmful to the quality of the survey sample? How can we monitor these?
• Are there ways to implement a random walk in a way that sampling error is minimised?
• How can we improve methods at the doorstep to reduce sampling error, i.e., tackle interviewers or respondents (self-)selection?
• How is the data quality affected by different fieldwork practices in comparative or longitudinal studies? How much could/should we harmonise?
• How do house changes affect fieldwork?

We welcome contributions from various cultural contexts, survey practitioners, secondary survey data users, and academic researchers.