Explore the vast range of titles available.

Die Entstehung moderner Nationalstaaten ging historisch mit der Entwicklung spezifischer Verständnisse von Individuum, Bevölkerung und Gesellschaft, räumlichen Grenzen und Zugehörigkeiten einher. Über die amtliche Statistik, die ebenfalls in diesem Kontext entstanden ist, wurden diese politischen Konzepte zu messbaren Kategorien und empirischen Realitäten. Gegenstand dieses Beitrags ist die spezifische Konstitution von \"Gesellschaft\" durch die amtliche Statistik. Die Relevanz des statistischen Gesellschaftsverständnisses begründet sich dadurch, dass sie die Grundlage für Stichprobenziehungen in der standardisierten Sozialforschung und damit auch für die kulturvergleichende Sozialforschung bildet: Als Schlüssel zur Verallgemeinerung von Forschungsergebnissen von wenigen Fällen auf größere Maßstäbe erfordert die standardisierte Forschung Stichproben aus angebbaren Grundgesamtheiten. Typischerweise handelt es sich dabei um Register der amtlichen Statistik wie z.B. Einwohnermeldeämter. Dies wird in der Literatur jedoch als Container-Ansatz von Gesellschaft kritisiert, weil eine Kongruenz zwischen (nationalem) Territorium, Kultur und Gesellschaft angenommen wird, statt deren Verhältnis zu analysieren. Dies ist nicht nur eine methodische Schwachstelle, vielmehr affirmiert und naturalisiert diese Stichprobenstrategie den nationalen Rahmen von Gesellschaft und Kultur. Dadurch werden transnationale soziale Beziehungen und Identitätsrahmen verborgen. Hintergrund für die Analyse spezifischer Probleme und Versäumnisse in der amtlichen Statistik bildet die Kritik am skizzierten territorialen Gesellschaftsbegriff, die im Folgenden vorgestellt wird und sich insbesondere auf die Mobilitäts- und Migrationsforschung stützt.

The author compares mass data with survey data and other process-generated data and discusses their relevance for historical, historical social science and sociological research. After summarizing the current state of methodological knowledge on public administrational data, she concludes that the discussion on mass data has to be re-opened. She suggests a framework for such a discussion and links the older German discussion from the 1970s and 1980s to the discussion newly arising. She suggests that the major issues are (a) data lore and measurement quality; (b) data selection and sampling problems; (c) archiving and statistical programmes and (d) data preparation. After summing up the state of the debate, the authors suggests which questions should be answered in future research.

In recent years, studies of historical populations have shifted from tracing large-scale processes to analyzing longitudinal micro data in the form of 'life histories'. This approach expands the scope of social history by integrating data on a range of life course events. The complexity of life-course analysis, however, has limited most researchers to working with one specific database. We discuss methodological problems raised by longitudinal historical data and the challenge of converting life histories into rectangular datasets compatible with statistical analysis systems. The logical next step is comparing life courses across local and national databases, and we propose a strategy for sharing historical longitudinal data based on an intermediate data structure (IDS) that can be adopted by all databases. We describe the benefits of the IDS approach and activities that will advance the goals of simplifying and promoting research with longitudinal historical data.

This article introduces the data integration and consolidation process of the research data base of the Institute for Employment Research. The data are process generated data and stem from various, autonomous administrative processes. This fact implies that there are manifold inconsistencies between the data from the different data sources. This opens up the methodological problem of a successful consolidation of inconsistencies. Two contrarian strategies to handle this methodological problem are discussed and the solution in the IAB-data base is presented.

Surveys often cope with special problems: gaps in retrospection appear or respondents could not provide details. Sometimes these problems can be solved by using additional qualitative information. another — so far disregarded — possibility is to use process-generated data to expand survey data. The focus of this article is on the potentials and problems of linking administrative and survey data. In particular this is shown by comparison of retrospective survey information on employment cycles and the according process-generated data.

This article discusses ethnic classification in the censuses in order to prepare its use as an independent variable in for instance demographic studies. The availability of census data and other public administrative data are increasing, also cross-nationally. In order to use these consistently in analyses, variables and categories have to remain the same over all measurement points, and the same type of person should whenever possible be classified and categorized in the same way. Using the case of ethnicity variables in Norwegian censuses, the article a) illustrates that with process-produced data, the contents of the original manuscripts are not necessarily comparable over time and space; b) it then discusses factors leading to these incompatibilities and c) suggests how to harmonize the inconsistencies.

The original official purpose of the U.S. Census was to gather information to design political districts of approximately the same size. Increasingly Census data has been used for descriptive and social scientific purposes. This paper examines how the category of \"occupation\" has changed and looks at several issues which arise in comparing the present day workforce with the workforce in past decades. Changes in concepts, practices, and historical context have greatly affected how many persons were recorded as having occupations, especially for married women, American Indians, teenagers, and people who have ceased paid work.

Until administrative data are available as research datasets, they are passed through many organizational units and stored in different formats. The transformations of collected data to a data warehouse and further the integration of data from several operational sources to an integrated dataset for research projects include various mappings of identifiers and variables. A particular challenge arises, whenever one of the intermediate products changes. The resulting difficulties are not only technical in nature, but may well lie in aspects of the theoretical interpretation in a particular research context. For long-term research projects, it is essential to ensure comparability between several versions of this dataset. So the main task resulting from changes in the data sources is to ensure that observations of a former version of a research dataset can be identified after these changes. A case study of the Integrated Employment Biographies (IEB) is presented as an example of these problems. In a first step reasons for changes in the data sources and the methodological problems of transformation between several versions of a research dataset are highlighted. In a second step some tests of variables fundamental for research analysis and stratification are presented.

Process-generated and administrative datasets have become increasingly important for labour market research over the past ten years. Major advantages of this data are large sample sizes, absence of retrospective gaps and unit nonresponses. Nevertheless, the quality and validity of the information remain unclear. This paper contributes to this subject, focusing on the variation of research results due to alternative data cleansing procedures. In particular, the paper uses the general set up for data cleaning proposed by Wunsch/Lechner (2008) in evaluating the training programmes in Germany. First results are limited to the sensitivity of the construction of the sample populations used for the counterfactuals analysis. The results emphasize that sample construction seems to be robust to the scenario used for the data cleansing.

In Germany, process-generated data and survey data on firms are collected by different data producers. Each data producer provides access for researchers to its data, but the combination of datasets from different producers is not possible at the moment. A new project (KombiFiD) aims to overcome this limitation: firm data collected by the German Statistical Offices, and Deutsche Bundesbank and the Federal Employment Agency will be linked for the first time. The project aims are twofold: to gauge the possibilities of linking selected datasets beyond the limits of individual labour market data producers and to provide a combined dataset to science, thereby creating new research opportunities. This paper describes the project, the selected datasets and explains potential matching problems. In this context we address e.g. the advantages and disadvantages of survey and process-generated data and some challenges we expect within the project.