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Abstract

A KeyGraph-like algorithm, which incorporates the concept of structural importance with association rules mining, for analyzing closed-ended questionnaire data is presented in this paper. The proposed algorithm transforms the questionnaire data into a directed graph, and then applies association rules mining and clustering procedures, whose parameters are determined by gradient sensitivity analysis, as well as correlation analysis in turn to the graph. As a result, both statistically significant and other cryptic events are successfully unveiled. A questionnaire survey data from an instructional design application has been analyzed by the proposed algorithm. Comparing to the results of statistical methods, which elicited almost no information, the proposed algorithm successfully identified three cryptic events and provided five different strategies for designing instructional activities. The preliminary experimental results indicated that the algorithm works out for analyzing closed-ended questionnaire survey data.

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References

  1. Sakai, T.: Questionnaire design, Marketing Research and Statistical Analysis. DrSmart Press, Taipei (2004) (in Chinese)

    Google Scholar 

  2. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of the 20th VLDB Conference, pp. 487–499 (1994)

    Google Scholar 

  3. Nara, Y., Ohsawa, Y.: Application to Questionnaire Analysis. In: Chance Discovery, pp. 351–366. Springer, Heidelberg (2003)

    Google Scholar 

  4. Ohsawa, Y., Benson, N., Yachida, M.: Keygraph: Automatic indexing by co-occurrence graph based on building construction metaphor. In: Proceedings of the Advances in Digital Libraries Conference (1998)

    Google Scholar 

  5. Tamura, H., Washida, Y., Ohsawa, Y.: Emerging Scenarios by Using DDM: A Case Study for Japanese Comic Marketing. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds.) KES 2004. LNCS, vol. 3213, pp. 847–854. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  6. Mizuno, M.: Chance Discovery for Consumers. In: Chance Discovery, pp. 367–382. Springer, Heidelberg (2003)

    Google Scholar 

  7. Ohsawa, Y.: Modelling the Process of Chance Discovery. In: Chance Discovery, pp. 1–15. Springer, Heidelberg (2003)

    Google Scholar 

  8. Keller, J., Kopp, T.: Application of the ARCS Model to Motivational Design. In: Instructional Theories in Action: Lessons Illustrating Selected Theories, pp. 289–320. Lawrence Erlbaum, Mahwah (1997)

    Google Scholar 

  9. Suzuki, K., Nishibuchi, A., Yamamoto, M., Keller, J.: Development and evaluation of website to check instructional design based on the arcs motivation model. Information and Systems in Education 2(1), 63–69 (2004)

    Google Scholar 

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© 2006 Springer-Verlag Berlin Heidelberg

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Wang, LH., Hong, CF., Hsu, CL. (2006). Closed-Ended Questionnaire Data Analysis. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2006. Lecture Notes in Computer Science(), vol 4253. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893011_1

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  • DOI: https://doi.org/10.1007/11893011_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-46542-3

  • Online ISBN: 978-3-540-46544-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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