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  4. Design and application of Self-Generated Identification Codes (SGICs) for matching longitudinal data
 
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Design and application of Self-Generated Identification Codes (SGICs) for matching longitudinal data

Publikationstyp
Conference Paper
Date Issued
2016-09
Sprache
English
Author(s)
Direnga, Julie  
Timmermann, Dion  orcid-logo
Lund, Jorrid  
Kautz, Christian  
Institut
Abteilung für Fachdidaktik der Ingenieurwissenschaften Z-1  
Konstruktion und Festigkeit von Schiffen M-10  
Zentrum für Lehre und Lernen ZLL  
TORE-URI
http://hdl.handle.net/11420/6288
Citation
Annual Conference of the European Society for Engineering Education - Engineering Education on Top of the World: Industry-University Cooperation, SEFI: (2016-09)
Contribution to Conference
44th Annual Conference of the European Society for Engineering Education - Engineering Education on Top of the World: Industry-University Cooperation, SEFI 2016  
In this article, we presented a list of criteria for high quality SGIC-items and discussed the advantages and disadvantages of our chosen items for the purpose of pre-/post-testing and possible application in long-term study designs. We compared a handwritten and a bubble-sheet version of code forms and presented the algorithm used for matching. With accepting off-1 matches, we could improve our matching rate to 76 % compared to 72 % matching via official matriculation numbers. A handwriting analysis indicates no false-positive and only 8 % false-negative matches. Based on our results, we conclude that the advantages of using SGICs can outweigh the disadvantages, providing that the items and the matching algorithm are chosen carefully. We thus encourage other researchers to consider using SGICs for their data linkage.
Subjects
Matching algorithm
Subject-generated coding
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