Inductive item tree analysis: Corrections, improvements, and comparisons
AbstractThere 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.
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Bibliographic InfoArticle provided by Elsevier in its journal Mathematical Social Sciences.
Volume (Year): 58 (2009)
Issue (Month): 3 (November)
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Web page: http://www.elsevier.com/locate/inca/505565
Inductive item tree analysis Knowledge space theory Deriving surmise relations;
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- Schrepp, Martin, 2007. "On the evaluation of fit measures for quasi-orders," Mathematical Social Sciences, Elsevier, vol. 53(2), pages 196-208, March.
- 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.
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