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Comparing Optimization Algorithms for Item Selection in Mokken Scale Analysis

Journal of Classification volume 30, pages 75–99 (2013)Cite this article

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.

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Authors and Affiliations

  1. Department of Methodology and Statistics, Tilburg University, P. O. Box 90153, 5000, LE, Tilburg, The Netherlands

    J. Hendrik Straat, L. Andries van der Ark & Klaas Sijtsma

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  1. J. Hendrik Straat
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  2. L. Andries van der Ark
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  3. Klaas Sijtsma
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Correspondence to J. Hendrik Straat.

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Straat, J.H., van der Ark, L.A. & Sijtsma, K. Comparing Optimization Algorithms for Item Selection in Mokken Scale Analysis. J Classif 30, 75–99 (2013). https://doi.org/10.1007/s00357-013-9122-y

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  • DOI: https://doi.org/10.1007/s00357-013-9122-y

Keywords

  • Item selection
  • Genetic algorithm
  • Mokken scaling
  • Test construction