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Data-Revision Module—A Beneficial Tool to Support Autobiographical Memory in Life-Course Studies

  • Michael RulandEmail author
  • Katrin Drasch
  • Ralf Künster
  • Britta Matthes
  • Angelika Steinwede
Chapter

Abstract

The key objective of the National Educational Panel Study (NEPS) is to enable analyses of the development of competencies, educational processes, educational decisions, and returns to education throughout the life span. These analyses are only possible by collecting complete and consistent educational and employment histories as well as the relevant contexts in which these histories are embedded, in other words, life-course data. In this respect, the most important challenge is remembering life histories retrospectively. To support both the cognitive memory capacity and the temporal integration of reported episodes in the life course, we decided to use a modular technique for collecting life-course data retrospectively. However, modularization makes it more difficult for respondents to recall the temporal integration of the episodes reported in the different life domains. To compensate for this disadvantage, we implemented a data-revision module that integrates all reported episodes from the different life domains immediately after collecting all relevant life-course data. In the data-revision module, the interviewer can edit all existing temporal inconsistencies in the life course in collaboration with the respondent by correcting the time span of episodes, by deleting and inserting episodes, and by clarifying overlaps of episodes. The module also pays attention to episodes with incomplete or missing calendar dates that can—by using estimates—be included in the life-course data and be edited directly during the interview in collaboration with the respondent. The result is a marked improvement in the data quality and validity of the recorded life histories.

Keywords

Life Domain Longitudinal Module Autobiographical Memory Computer Assist Telephone Interview National Educational Panel Study 
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.

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Copyright information

© Springer Fachmedien Wiesbaden 2016

Authors and Affiliations

  • Michael Ruland
    • 1
    Email author
  • Katrin Drasch
    • 2
  • Ralf Künster
    • 3
  • Britta Matthes
    • 4
  • Angelika Steinwede
    • 1
  1. 1.BonnDeutschland
  2. 2.ErlangenDeutschland
  3. 3.BerlinDeutschland
  4. 4.NürnbergDeutschland

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