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Inductive item tree analysis: Corrections, improvements, and comparisons

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  • Sargin, Anatol
  • Ünlü, Ali

Abstract

There are various methods in knowledge space theory for building knowledge structures or surmise relations from data. Few of them have been thoroughly analyzed, making it difficult to decide which of these methods provides good results and when to apply each of the methods. In this paper, we investigate the method known as inductive item tree analysis and discuss the advantages and disadvantages of this algorithm. In particular, we introduce some corrections and improvements to it, resulting in two newly proposed algorithms. These algorithms and the original inductive item tree analysis procedure are compared in a simulation study and with empirical data.

Suggested Citation

  • Sargin, Anatol & Ünlü, Ali, 2009. "Inductive item tree analysis: Corrections, improvements, and comparisons," Mathematical Social Sciences, Elsevier, vol. 58(3), pages 376-392, November.
    • Handle: RePEc:eee:matsoc:v:58:y:2009:i:3:p:376-392
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    References listed on IDEAS

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    1. Schrepp, Martin, 1999. "On the empirical construction of implications between bi-valued test items," Mathematical Social Sciences, Elsevier, vol. 38(3), pages 361-375, November.
    2. Schrepp, Martin, 2007. "On the evaluation of fit measures for quasi-orders," Mathematical Social Sciences, Elsevier, vol. 53(2), pages 196-208, March.
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    Cited by:

    1. Goran Savić & Milan Segedinac & Zora Konjović & Milan Vidaković & Radoslav Dutina, 2023. "Towards a Domain-Neutral Platform for Sustainable Digital Twin Development," Sustainability, MDPI, vol. 15(18), pages 1-23, September.
    2. repec:jss:jstsof:37:i02 is not listed on IDEAS
    3. Ünlü, Ali & Schrepp, Martin, 2015. "Untangling comparison bias in inductive item tree analysis based on representative random quasi-orders," Mathematical Social Sciences, Elsevier, vol. 76(C), pages 31-43.

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