Comparing Optimization Algorithms for Item Selection in Mokken Scale Analysis

J. Straat (), L. Ark and Klaas Sijtsma

Journal of Classification, 2013, vol. 30, issue 1, 75-99

Abstract: Mokken scale analysis uses an automated bottom-up stepwise item selection procedure that suffers from two problems. First, when selected during the procedure items satisfy the scaling conditions but they may fail to do so after the scale has been completed. Second, the procedure is approximate and thus may not produce the optimal item partitioning. This study investigates a variation on Mokken’s item selection procedure, which alleviates the first problem, and proposes a genetic algorithm, which alleviates both problems. The genetic algorithm is an approximation to checking all possible partitionings. A simulation study shows that the genetic algorithm leads to better scaling results than the other two procedures. Copyright Springer Science+Business Media New York 2013

Keywords: Item selection; Genetic algorithm; Mokken scaling; Test construction (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (7)

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DOI: 10.1007/s00357-013-9122-y

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