Comparing optimization algorithms for item selection in Mokken scale analysis

J.H. Straat, L.A. van der Ark, K. Sijtsma

Research output: Contribution to journalArticleScientificpeer-review

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.
Keywords: Item selection, Genetic algorithm, Mokken scaling, Test construction
Original languageEnglish
Pages (from-to)75-99
JournalJournal of Classification
Volume30
Issue number1
DOIs
Publication statusPublished - 2013

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Optimization Algorithm
scaling
Genetic Algorithm
selection procedure
Selection Procedures
Scaling
Partitioning
test construction
Bottom-up
Simulation Study
simulation
Approximation
Genetic algorithm

Cite this

Straat, J.H. ; van der Ark, L.A. ; Sijtsma, K. / Comparing optimization algorithms for item selection in Mokken scale analysis. In: Journal of Classification. 2013 ; Vol. 30, No. 1. pp. 75-99.
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Comparing optimization algorithms for item selection in Mokken scale analysis. / Straat, J.H.; van der Ark, L.A.; Sijtsma, K.

In: Journal of Classification, Vol. 30, No. 1, 2013, p. 75-99.

Research output: Contribution to journalArticleScientificpeer-review

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