There has been a long discussion on how administrative mass can be made amenable for social research. We trace this discussion back to the suggestions by Bick and Müller (1984) (also Baur 2009) who suggested a framework for analyzing administrative data and show that the difficulties in using this kind of data are rather similar to modern digital data. It follows that suggestions for overcoming shortcomings of administrative data can be applied in similar way to digital data.
While we are particularly interested in questions on how a social phenomenon can be measured by means of data from different sources which are obviously (or at least indirectly) related to the phenomenon of interest – problems and solutions can only be shown for specific phenomena. For reasons of illustration and application we refer to the social phenomenon of corruption. Issues pertaining different measurement sources have become relevant in recent years because, in the wake of the digital turn, new data sources have become available. These new sources, such as media and regional (or spatial) data, augment the array of classical sources, such as statements (e.g. intentions of actions) by persons regarding corrupt practices (survey data) or the number of cases registered by prosecuting authorities (administrative data).
Although there are several good examples of administrative or digital data working as a complement or a substitute for data generated by scientific processes, such as surveys, there are still challenges to overcome in measurement and scientific application.
References
Baur, N. (2009). Problems of Linking Theory and Data in Historical Sociology and Longitudinal Research. Historical Social Research 34(1), 7-21.
Bick, W., & Müller, P. J. (1984). Sozialwissenschaftliche Datenkunde für prozeßproduzierte Daten. Entstehungsbedingungen und Indikatorenqualität. In: Bick, W., Mann, R. & Müller, P. J. (Eds.). Sozialforschung und Verwaltungsdaten. Historisch-Sozialwissenschaftliche Forschungen Volume 17. Stuttgart: Klett-Cotta, 123-159.
There has been a long discussion on how administrative mass can be made amenable for social research. We trace this discussion back to the suggestions by Bick and Müller (1984) (also Baur 2009) who suggested a framework for analyzing administrative data and show that the difficulties in using this kind of data are rather similar to modern digital data. It follows that suggestions for overcoming shortcomings of administrative data can be applied in similar way to digital data.
While we are particularly interested in questions on how a social phenomenon can be measured by means of data from different sources which are obviously (or at least indirectly) related to the phenomenon of interest – problems and solutions can only be shown for specific phenomena. For reasons of illustration and application we refer to the social phenomenon of corruption. Issues pertaining different measurement sources have become relevant in recent years because, in the wake of the digital turn, new data sources have become available. These new sources, such as media and regional (or spatial) data, augment the array of classical sources, such as statements (e.g. intentions of actions) by persons regarding corrupt practices (survey data) or the number of cases registered by prosecuting authorities (administrative data).
Although there are several good examples of administrative or digital data working as a complement or a substitute for data generated by scientific processes, such as surveys, there are still challenges to overcome in measurement and scientific application.
References
Baur, N. (2009). Problems of Linking Theory and Data in Historical Sociology and Longitudinal Research. Historical Social Research 34(1), 7-21.
Bick, W., & Müller, P. J. (1984). Sozialwissenschaftliche Datenkunde für prozeßproduzierte Daten. Entstehungsbedingungen und Indikatorenqualität. In: Bick, W., Mann, R. & Müller, P. J. (Eds.). Sozialforschung und Verwaltungsdaten. Historisch-Sozialwissenschaftliche Forschungen Volume 17. Stuttgart: Klett-Cotta, 123-159.
There has been a long discussion on how administrative mass can be made amenable for social research. We trace this discussion back to the suggestions by Bick and Müller (1984) (also Baur 2009) who suggested a framework for analyzing administrative data and show that the difficulties in using this kind of data are rather similar to modern digital data. It follows that suggestions for overcoming shortcomings of administrative data can be applied in similar way to digital data.
While we are particularly interested in questions on how a social phenomenon can be measured by means of data from different sources which are obviously (or at least indirectly) related to the phenomenon of interest – problems and solutions can only be shown for specific phenomena. For reasons of illustration and application we refer to the social phenomenon of corruption. Issues pertaining different measurement sources have become relevant in recent years because, in the wake of the digital turn, new data sources have become available. These new sources, such as media and regional (or spatial) data, augment the array of classical sources, such as statements (e.g. intentions of actions) by persons regarding corrupt practices (survey data) or the number of cases registered by prosecuting authorities (administrative data).
Although there are several good examples of administrative or digital data working as a complement or a substitute for data generated by scientific processes, such as surveys, there are still challenges to overcome in measurement and scientific application.
References
Baur, N. (2009). Problems of Linking Theory and Data in Historical Sociology and Longitudinal Research. Historical Social Research 34(1), 7-21.
Bick, W., & Müller, P. J. (1984). Sozialwissenschaftliche Datenkunde für prozeßproduzierte Daten. Entstehungsbedingungen und Indikatorenqualität. In: Bick, W., Mann, R. & Müller, P. J. (Eds.). Sozialforschung und Verwaltungsdaten. Historisch-Sozialwissenschaftliche Forschungen Volume 17. Stuttgart: Klett-Cotta, 123-159.
Keywords
Administrative data
Corruption
Digital data
Survey data
