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 |
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| 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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| AbstractList | 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. 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.[PUBLICATION 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. Adapted from the source document. |
| Author | van der Ark, L. Andries Sijtsma, Klaas Straat, J. Hendrik |
| Author_xml | – sequence: 1 givenname: J. Hendrik surname: Straat fullname: Straat, J. Hendrik email: hendrikstraat@gmail.com organization: Department of Methodology and Statistics, Tilburg University – sequence: 2 givenname: L. Andries surname: van der Ark fullname: van der Ark, L. Andries organization: Department of Methodology and Statistics, Tilburg University – sequence: 3 givenname: Klaas surname: Sijtsma fullname: Sijtsma, Klaas organization: Department of Methodology and Statistics, Tilburg University |
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| Cites_doi | 10.1007/978-3-662-03315-9 10.1037/1082-989X.1.3.293 10.1007/s11336-000-0862-3 10.1111/j.1745-3984.1998.tb00525.x 10.1177/0146621603027003001 10.1007/s11336-010-9147-7 10.1002/acp.2350050305 10.1027//1015-5759.19.1.24 10.1177/014662168601000306 10.1177/014662168500900204 10.2307/1165182 10.1177/0146621606295196 10.1007/s11336-004-1257-7 10.1177/014662169501900404 10.1007/s11336-007-9034-z 10.1201/9781420035933 10.1177/014662168200600404 10.1007/BF02294536 10.1007/BF02294219 10.1007/BF02294462 10.1177/0146621603259277 10.1177/0146621605286315 10.1007/BF02293705 10.1037/1082-989X.12.1.105 10.1007/BF02294555 10.1515/9783110813203 10.4135/9781412984676 |
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| Keywords | Mokken scaling Item selection Genetic algorithm Test construction |
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| SubjectTerms | 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 |
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| Title | Comparing Optimization Algorithms for Item Selection in Mokken Scale Analysis |
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