The DACSEIS Project

Data quality has become increasingly important in social and economic statistics, especially when data are gained from various sources obtained using different methods. In many cases, measuring data quality is strongly connected to variance estimation as one error source. The DACSEIS project investigates variance estimation methods for complex surveys in the context of an applicable and comparable quality measurement of European household surveys. The aim of the project is to deliver recommendations of how to measure data quality in complex surveys that take various aspects of error sources into consideration and allow the handling of different components of the measures. This will include a complex Monte Carlo simulation study in a practical environment, e.g. in synthetic, but realistic, universes that are close to selected European household surveys, to test the wide range of theoretical methodology in practice

On the simulation of complex universes in the case of applying the german microcensus

The aim of the DACSEIS project is to deliver recommendations on the use of variance estimators under complex survey designs in the presence of non-response. Since mathematical comparisons on the efficiency of variance estimation methods in this field are generally unavailable or lead to irrelevant results, adequate simulation studies have to be carried out that are based on realistic data sets. To be able to carry out a simulation study in the frame of complex designs one has to draw samples from a universe respecting for the true sampling design. However, in many cases, no data or only outdated data are available for the universe which leads to the need of adequately generating a micro data set from the sample. Within this paper, a procedure of generating the universe for the German microcensus, which is a 1 sample of the population living in Germany, will be presented. The procedure allows for an adequate consideration of the individual information on a limited data set and can therefore be used as a basis for the simulations on variance estimation methods on the German microcensus data

Quality and Sensitivity of Composite Indicators for Sustainable Development

Composite indicators can be understood as a summary of well-chosen and relevant sub-indicators which are combined into a single number. Their aim is to represent a multi-dimensional construct and map the performance of entities such as countries or companies. These multidimensional constructs are for example sustainability, poverty or well-being. Composite indicators are widely applied in various disciplines such as social or economic research and benefit from their apparent ease of interpretation. In the context of the Sustainable Development Framework a composite indicator over all 17 sustainable development goals, as been proposed. As composite indicators are commonly applied in highly sensitive areas this, urges the need to discuss methodical advantages and disadvantages as well as their adequacy for performance comparisons. In this paper we discuss and illustrate quality issues with regard to aspects of the subjective choices made in the construction process of composite indicators, imputation of missing data and the survey design. As an example we construct a composite indicator on sustainable economic development using data of the Sustainable Development framework. Furthermore, we exemplify and discuss strategies and methods for the quality assessment of a composite indicator

Synthetic data for open and reproducible methodological research in social sciences and official statistics

In der Forschung nehmen Vergleichbarkeit und Reproduzierbarkeit immer mehr an Bedeutung zu. Die empirische Forschung profitiert dabei von Forschungsdatenzentren und Scientific Use Files. Für angewandte Methodenforschung dagegen sind geeignete Datenquellen kaum verfügbar, obwohl gerade in den Wirtschafts- und Sozialwissenschaften komplexe Stichprobendesigns bei der Entwicklung und Anwendung von Schätzmethoden berücksichtigt werden müssen. In dieser Arbeit wird ein synthetischer, jedoch realistischer Datensatz vorgestellt, der gerade die Evaluierung und Entwicklung von Schätzmethoden in den Sozial- und Wirtschaftswissenschaften unterstützt. Der Schwerpunkt liegt dabei auf vergleichbarer und reproduzierbarer Forschung in einer realistischen Umgebung in Bezug auf Individual- und Haushaltsdaten. Dieser Datensatz wird der Forschungsgemeinde frei zur Verfügung gestellt.Open and reproducible research receives more and more attention in the research community. Whereas empirical research may benefit from research data centres or scientific use files that foster using data in a safe environment or with remote access, methodological research suffers from the availability of adequate data sources. In economic and social sciences, an additional drawback results from the presence of complex survey designs in the data generating process, that has to be considered when developing and applying estimators. In the present paper, we present a synthetic but realistic dataset based on social science data, that fosters evaluating and developing estimators in social sciences. The focus is on supporting comparable and reproducible research in a realistic framework providing individual and household data. The outcome is provided as an open research data resource

Datenerhebung bei Mietspiegeln: Überblick und Einordnung aus Sicht der Statistik

Der Artikel diskutiert die verschiedenen Methoden bei der Datenerhebung von Mietspiegeln. Es werden Vor- und Nachteile der in der Praxis zu findenden Methoden diskutiert und aus dem statistischen Blickwinkel beleuchtet. Dabei gehen wir den drei Fragen nach: Wer wird befragt? Wie wird befragt? Wie erfolgt die Stichprobenziehung? Neben statistischen Aspekten werden die Mietspiegel der 30 größten Städte als Beispiel herangezogen, um aufzuzeigen, dass die angewandte Methodik in der Praxis sehr heterogen ist

Das Stichprobendesign des registergestützten Zensus 2011

"Im Rahmen der europaweiten Zensus-Erhebungsrunde im Jahr 2011 wird zum ersten Mal seit 1987 auch im vereinigten Deutschland wieder eine Volkszählung stattfinden, diesmal allerdings nicht in Form einer Vollerhebung, sondern in Form einer kosten- und ressourcenschonenden registergestützten Erhebung. Diese wird flankiert durch eine Haushaltsstichprobe, aus der erstens in den Registern nicht erfasste Informationen gewonnen werden sollen und zweitens eine Abschätzung der Zahl der Karteileichen (KAL) und Fehlbestände (FEB) in den Melderegistern erfolgen soll. Aus den Register- und Stichprobendaten sollen möglichst verlässliche und genaue Schätzungen der Totalwerte vorgenommen werden. Ziel des von DESTATIS eingesetzten Stichprobenforschungsprojektes ist es, Antworten auf die Frage zu geben, welches Stichprobendesign unter den gegebenen Restriktionen empfohlen werden kann. Darüber hinaus sollen Schätzstrategien entwickelt werden, die zur Verwendung im Zensus 2011 vorgeschlagen werden können. Der vorliegende Aufsatz stellt einige wichtige Erkenntnisse aus dem Forschungsprojekt dar, wobei ein Schwerpunkt auf der Darstellung eines optimalen Stichprobendesigns liegt." (Autorenreferat)"Within the context of the Europe-wide census elicitation in 2011 there will be the first population census in reunified Germany. In contrast to the last German census in 1987, where all households were interviewed, the new census will be conducted by means of a cost- and resource-effective register-assisted census. In addition to the register information, a household sample will be drawn. On the one hand this sample will provide information that is not included in the register, on the other hand it will allow for the estimation of over- and undercounts in the register. Reliable estimates for total values of interest are to be derived from the register and sample data. The aim of the research project, which was initiated by DESTATIS, is to elaborate an efficient sample design as well as to develop estimation strategies which allow accurate estimates for the census 2011. This article presents some important findings from the research project. However, one focus is on the description of an optimal sample design." (author's abstract

On the role of data, statistics and decisions in a pandemic

A pandemic poses particular challenges to decision-making because of the need to continuously adapt decisions to rapidly changing evidence and available data. For example, which countermeasures are appropriate at a particular stage of the pandemic? How can the severity of the pandemic be measured? What is the effect of vaccination in the population and which groups should be vaccinated first? The process of decision-making starts with data collection and modeling and continues to the dissemination of results and the subsequent decisions taken. The goal of this paper is to give an overview of this process and to provide recommendations for the different steps from a statistical perspective. In particular, we discuss a range of modeling techniques including mathematical, statistical and decision-analytic models along with their applications in the COVID-19 context. With this overview, we aim to foster the understanding of the goals of these modeling approaches and the specific data requirements that are essential for the interpretation of results and for successful interdisciplinary collaborations. A special focus is on the role played by data in these different models, and we incorporate into the discussion the importance of statistical literacy, and of effective dissemination and communication of findings

Authors' response: on the role of data, statistics and decisions in a pandemic

"We would like to thank the discussants once more for adding their thoughtful comments.