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Untangling comparison bias in inductive item tree analysis based on representative random quasi-orders

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  • Ünlü, Ali
  • Schrepp, Martin

Abstract

Inductive item tree analysis (IITA) comprises three data analysis algorithms for deriving quasi-orders to represent reflexive and transitive precedence relations among binary variables. In previous studies, when comparing the IITA algorithms in simulations, the representativeness of the sampled quasi-orders was not considered or implemented only unsatisfactorily. In the present study, we show that this issue yields non-representative samples of quasi-orders, and thus biased or incorrect conclusions about the performance of the IITA algorithms used to reconstruct underlying relational dependencies. We report the results of a new, truly representative simulation study, which corrects for these problems and that allows the algorithms to be compared in a reliable manner.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:matsoc:v:76:y:2015:i:c:p:31-43
    DOI: 10.1016/j.mathsocsci.2015.03.005
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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, 2006. "ITA 2.0: A Program for Classical and Inductive Item Tree Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 16(i10).
    3. Ünlü, Ali & Sargin, Anatol, 2010. "DAKS: An R Package for Data Analysis Methods in Knowledge Space Theory," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 37(i02).
    4. Sargin, Anatol & Ünlü, Ali, 2009. "Inductive item tree analysis: Corrections, improvements, and comparisons," Mathematical Social Sciences, Elsevier, vol. 58(3), pages 376-392, November.
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