Figure 2 - uploaded by Kees van Berkel
Content may be subject to copyright.
Source publication
A challenge for the National Statistical Institutes is to produce reliable statistics with a limited budget for data collection. During the past years, many surveys at Statistics Netherlands were redesigned to reduce costs and to increase or maintain response rates. From 2018 onwards, adaptive survey design has been applied in several social survey...
Context in source publication
Context 1
... overall realised CAWI response rate is 3.5 percentage points greater than estimated. The relative difference (r -e)/e is largest in group 1 and smallest in group 2. Figure 2 contains the weekly CATI-and CAPI-sampling fractions per target group. As established by design, CAPI is mainly applied in target groups with a low number and CATI in target groups with a high number. ...
Similar publications
Cross-national surveys run the risk of differential survey errors, where data collected vary in quality from country to country. Responsive and adaptive survey designs (RASDs) have been proposed as a way to reduce survey errors, by leveraging auxiliary variables to inform fieldwork efforts, but have rarely been considered in the context of cross-na...
Citations
Adaptive survey design is a framework for making data-driven decisions about survey data collection operations. This article discusses open questions related to the extension of adaptive principles and capabilities when capturing data from multiple data sources. Here, the concept of “design” encompasses the focused allocation of resources required for the production of high-quality statistical information in a sustainable and cost-effective way. This conceptual framework leads to a discussion of six groups of issues including: (1) the goals for improvement through adaptation; (2) the design features that are available for adaptation; (3) the auxiliary data that may be available for informing adaptation; (4) the decision rules that could guide adaptation; (5) the necessary systems to operationalize adaptation; and (6) the quality, cost, and risk profiles of the proposed adaptations (and how to evaluate them). A multiple data source environment creates significant opportunities, but also introduces complexities that are a challenge in the production of high-quality statistical information.
Statistics Netherlands’ social surveys are based on a sequential mixed-mode data collection approach using web, telephone, and face-to-face interviewing. This article illustrates how Statistics Netherlands addressed the sudden, unforeseen loss of face-to-face interviews in social surveys amidst the COVID-19 pandemic. At the beginning of the pandemic, survey processes were immediately adjusted in several ways to mitigate the negative effects of respondent attrition. Where possible, sampled people initially assigned to face-to-face interviewing were motivated to respond through web or telephone to minimize the loss of response. At the same time regression analysis and simulation were conducted to obtain quantitative insight into the effects of losing face-to-face responses in the sequential mixed-mode designs. Furthermore, alternative model-based estimation procedures based on structural time series models were implemented to compensate for the bias that is a result of the loss of face-to-face responses. These initiatives are illustrated with applications to the Dutch Labor Force Survey, the Housing Survey, and the Health Survey.
