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The Performance of German Research Training Groups in Different Disciplinary Fields – An Empirical Assessment

  • Birgit UngerEmail author
  • Kerstin Pull
  • Uschi Backes-Gellner
Chapter
Part of the Higher Education Dynamics book series (HEDY, volume 32)

Abstract

In the early 1990s, the German Research Foundation established a new form of graduate education: the so-called Research Training Groups (RTGs). Comparable to PhD programmes in the US, RTGs offer a structured course-programme and a framework for collaborative research for young researchers who are to be integrated in the research activities and the scientific networks of the participating institutions. As a result, the time span needed to complete a doctorate was expected to decrease and the scientific visibility of doctoral and post-doctoral research was held to increase. However, the performance of German RTGs has not been evaluated as yet. In this chapter, we undertake a first step in that direction and assess the performance of German RTGs in different disciplines (humanities social sciences vs. natural life sciences) in two important respects: (1) Doctoral degrees as an immediate outcome of the graduate teaching undertaken and (2) scientific visibility of doctoral and post-doctoral students as measured by their publication output (supplemented by data on presentations). In our analysis of 86 German RTGs we present first empirical data on the two measures and are able to show that the performance of German RTGs varies considerably in and between the different disciplines. An additionally performed data envelopment analysis (DEA) reveals that even though a considerable share of RTGs operates at relative efficiency, there still seems to be remarkable potential for performance improvement.

Keywords

Data Envelopment AnalysisData Envelopment Analysis Life Science Doctoral Student Data Envelopment AnalysisData Envelopment Analysis Model Publication Output 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

Financial support by the German Research Foundation as well as support concerning the collection of the data is gratefully acknowledged.

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Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • Birgit Unger
    • 1
    Email author
  • Kerstin Pull
    • 1
  • Uschi Backes-Gellner
    • 2
  1. 1.Tübingen UniversityTübingenGermany
  2. 2.University of ZurichZurichSwitzerland

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