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Open access

Pim Ouwehand and Barry Schouten

Abstract

Short-term statistics (STS) are important early indicators of economic activity. The statistics are obligatory for all EU countries and also serve as input to national accounts. In most countries, short-term Statistics are based on business surveys. However, in recent years a number of countries have gradually replaced their business surveys with business VAT registry data. An important question is whether these surveys and registries are representative of the populations and whether representativity is stable in time. We apply R-indicators and partial R-indicators to measure the representativity of both kinds of data sources. We find large differences between different months of the year and between the two data sources. We discuss dual frame approaches that optimize the accuracy of STS statistics.

Open access

Joep Burger, Koen Perryck and Barry Schouten

Abstract

Adaptive survey designs (ASDs) optimize design features, given 1) the interactions between the design features and characteristics of sampling units and 2) a set of constraints, such as a budget and a minimum number of respondents. Estimation of the interactions is subject to both random and systematic error. In this article, we propose and evaluate four viewpoints to assess robustness of ASDs to inaccuracy of design parameter estimates: the effect of both imprecision and bias on both ASD structure and ASD performance. We additionally propose three distance measures to compare the structure of ASDs. The methodology is illustrated using a simple simulation study and a more complex but realistic case study on the Dutch Travel Survey. The proposed methodology can be applied to other ASD optimization problems. In our simulation study and case study, the ASD was fairly robust to imprecision, but not to realistic dynamics in the design parameters. To deal with the sensitivity of ASDs to changing design parameters, we recommend to learn and update the design parameters.

Open access

Asaph Young Chun, Barry Schouten and James Wagner

Open access

Asaph Young Chun, Steven G. Heeringa and Barry Schouten

Abstract

We discuss an evidence-based approach to guiding real-time design decisions during the course of survey data collection. We call it responsive and adaptive design (RAD), a scientific framework driven by cost-quality tradeoff analysis and optimization that enables the most efficient production of high-quality data. The notion of RAD is not new; nor is it a silver bullet to resolve all the difficulties of complex survey design and challenges. RAD embraces precedents and variants of responsive design and adaptive design that survey designers and researchers have practiced over decades. In this paper, we present the four pillars of RAD: survey process data and auxiliary information, design features and interventions, explicit quality and cost metrics, and a quality-cost optimization tailored to survey strata. We discuss how these building blocks of RAD are addressed by articles published in the 2017 JOS special issue and this special section. It is a tale of the three perspectives filling in each other. We carry over each of these three perspectives to articulate the remaining challenges and opportunities for the advancement of RAD. We recommend several RAD ideas for future research, including survey-assisted population modeling, rigorous optimization strategies, and total survey cost modeling.