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Recent years have seen an increase in the amount of statistics describing different phenomena based on “Big Data.” This term includes data characterized not only by their large volume, but also by their variety and velocity, the organic way in which they are created, and the new types of processes needed to analyze them and make inference from them. The change in the nature of the new types of data, their availability, and the way in which they are collected and disseminated is fundamental. This change constitutes a paradigm shift for survey research. There is great potential in Big Data, but there are some fundamental challenges that have to be resolved before its full potential can be realized. This report provides examples of different types of Big Data and their potential for survey research; it also describes the Big Data process, discusses its main challenges, and considers solutions and research needs.

This article describes a general framework for improving the quality of statistical programs in organizations that provide a continual flow of statistical products to users and stakeholders. The work stems from a 2011 mandate to Statistics Sweden issued by the Swedish Ministry of Finance to develop a system of quality indicators for tracking developments and changes in product quality and for achieving continual improvements in survey quality across a diverse set of key statistical products. We describe this system, apply it to a number of products at Statistics Sweden, and summarize key results and lessons learned. The implications of this work for monitoring and evaluating product quality in other statistical organizations are also discussed.

In recent years we have seen an increase in the amount of statistics in society describing different phenomena based on so called Big Data. The term Big Data is used for a variety of data as explained in the report, many of them characterized not just by their large volume, but also by their variety and velocity, the organic way in which they are created, and the new types of processes needed to analyze them and make inference from them. The change in the nature of the new types of data, their availability, the way in which they are collected, and disseminated are fundamental. The change constitutes a paradigm shift for survey research.

Cet article est l’introduction d’une thèse avec le même titre (fait au Départment de Statistique de l’université de Stockholm) et qui s’adresse à des problèmes associés avec la qualité des enquêtes par entretiens. L’intervieweur a plusieurs taches à accomplir et ils peuvent souvent commetre des erreurs. L’auteur présente quelques thèmes généraux ainsi que leur traitement habituel. Elle suggère ensuite des approches nouvelles et des idées pour des recherches futures sur la question de coopération dans les enquêtes et d’accomplir correctement l’entretien avec un répondant. Elle examine aussi certaines procédures pour la réduction des non-réponses en termes de coûts et de réduction d’erreurs. This article is the introduction to a thesis with the same title (done at the Department of Statistics of the University of Stockholm) which deals with some of the issues associated with the quality in interview surveys. The interviewer has many tasks, and some of them are extremely error-prone. The author describes some general themes and how these are usually handled. She also suggests some new approaches and ideas for further work, both when it comes to gaining cooperation and when it comes to doing a good job in administering the question-answer process. She also evaluates a specific procedure to reduce nonresponse errors in interview surveys in terms of costs and error reduction.