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A computer-assisted qualitative data analysis framework for the engineering management domain

Author

Listed:
  • Amirali Saeedi
  • Toni L. Doolen

Abstract

In this research, a computer assisted framework was developed to help researchers in conducting qualitative research. This framework leveraged the GATE platform, along with natural language processing and knowledge extraction techniques, to develop an automatic text annotation and summarisation system. A performance model, developed from the literature on lean manufacturing implementation strategies was employed, and a lexicon database for lean implementation practices was also developed. A unique dataset from a previous research study focusing on lean implementation practices was used to conduct this development and testing. A number of different summarisation techniques were developed and tested. A customised sensitivity analysis method was developed and used to systematically perform summarisation algorithms comparisons. The results of this study showed that using this framework at the early stages of a qualitative study has a great potential to reduce the time spent by researchers in annotating large datasets.

Suggested Citation

  • Amirali Saeedi & Toni L. Doolen, 2012. "A computer-assisted qualitative data analysis framework for the engineering management domain," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 4(1), pages 1-20.
    • Handle: RePEc:ids:injdan:v:4:y:2012:i:1:p:1-20
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    References listed on IDEAS

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    1. Yupo Chan & John Talburt & Terry M. Talley (ed.), 2010. "Data Engineering," International Series in Operations Research and Management Science, Springer, number 978-1-4419-0176-7, September.
    2. Quinn Taylor & Christophe Giraud-Carrier & Charles D. Knutson, 2010. "Applications of data mining in software engineering," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 2(3), pages 243-257.
    3. Anthony Scime & Gregg R. Murray & Lance Y. Hunter, 2010. "Testing terrorism theory with data mining," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 2(2), pages 122-139.
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