<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Adaptive and responsive survey designs rely on monitoring indicators based on paradata. This process can better inform fieldwork management if the indicators are paired with a benchmark, which relies on empirical information collected in the first phase of the fieldwork or, for repeated or longitudinal surveys, in previous rounds or waves. We propose the “fieldwork power” (fieldwork production per time unit) as an indicator for monitoring, and we simulate this for the European Social Survey (ESS) Round 7 in Belgium and in the Czech Republic. We operationalize the fieldwork power as the weekly number of completed interviews and of contacts, the ratio of the number of completed interviews to the number of contact attempts and to the number of refusals. We use a repeated measurement multilevel model, with surveys in the previous rounds of the European Social Survey as the macro level and the weekly fieldwork power as repeated measurements to create benchmarks. We also monitor effort and data quality metrics. The results show how problems in the fieldwork evolution can be detected by monitoring the fieldwork power and by comparing it with the benchmarks. The analysis also proves helpful regarding post-survey fieldwork evaluation, and links effort, productivity, and data quality.</p></abstract>

Adaptive and responsive survey designs rely on monitoring indicators based on paradata. This process can better inform fieldwork management if the indicators are paired with a benchmark, which relies on empirical information collected in the first phase of the fieldwork or, for repeated or longitudinal surveys, in previous rounds or waves. We propose the “fieldwork power” (fieldwork production per time unit) as an indicator for monitoring, and we simulate this for the European Social Survey (ESS) Round 7 in Belgium and in the Czech Republic. We operationalize the fieldwork power as the weekly number of completed interviews and of contacts, the ratio of the number of completed interviews to the number of contact attempts and to the number of refusals. We use a repeated measurement multilevel model, with surveys in the previous rounds of the European Social Survey as the macro level and the weekly fieldwork power as repeated measurements to create benchmarks. We also monitor effort and data quality metrics. The results show how problems in the fieldwork evolution can be detected by monitoring the fieldwork power and by comparing it with the benchmarks. The analysis also proves helpful regarding post-survey fieldwork evaluation, and links effort, productivity, and data quality.

Fieldwork Monitoring for the European Social Survey: An illustration with Belgium and the Czech Republic in Round 7

Journal of Official Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Adaptive and responsive survey designs rely on monitoring indicators based on paradata. This process can better inform fieldwork management if the indicators...

Fieldwork Monitoring for the European Social Survey: An illustration with Belgium and the Czech Republic in Round 7

Publicado en línea: 09 sept 2017

Páginas: 659 - 686

Recibido: 01 mar 2016

Aceptado: 01 abr 2017

DOI: https://doi.org/10.1515/jos-2017-0031

Palabras clave
Adaptive/responsive design, productivity metric, effort metric, data quality metric

© 2017 Caroline Vandenplas et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Fieldwork Monitoring for the European Social Survey: An illustration with Belgium and the Czech Republic in Round 7

Publicado en línea: 09 sept 2017

Páginas: 659 - 686

Recibido: 01 mar 2016

Aceptado: 01 abr 2017

DOI: https://doi.org/10.1515/jos-2017-0031

Palabras claveAdaptive/responsive design, productivity metric, effort metric, data quality metric

© 2017 Caroline Vandenplas et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Palabras clave
Adaptive/responsive design, productivity metric, effort metric, data quality metric