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

Ali Ünlü and Martin Schrepp

Mathematical Social Sciences, 2015, vol. 76, issue C, 31-43

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.

Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:eee:matsoc:v:76:y:2015:i:c:p:31-43

DOI: 10.1016/j.mathsocsci.2015.03.005

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