Comparing Optimization Algorithms for Item Selection in Mokken Scale Analysis

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 t...

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Veröffentlicht in:Journal of classification Jg. 30; H. 1; S. 75 - 99
Hauptverfasser: Straat, J. Hendrik, van der Ark, L. Andries, Sijtsma, Klaas
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer-Verlag 01.04.2013
Springer Nature B.V
Schlagworte:
Algorithms
Alleviation
Analysis
Bioinformatics
Classification
Genetic algorithms
Genetics
Marketing
Mathematics and Statistics
Measures
Optimization
Pattern Recognition
Psychometrics
Selection
Signal,Image and Speech Processing
Simulation
Statistical Theory and Methods
Statistics
Mokken scaling
Item selection
Genetic algorithm
Test construction
ISSN:0176-4268, 1432-1343
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Zusammenfassung: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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ISSN:0176-4268
1432-1343
DOI:10.1007/s00357-013-9122-y