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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, 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).
    2. 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).
    3. 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.
    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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