Mokken Scale Analysis Using Hierarchical Clustering Procedures

Autor/inn/en	van Abswoude, Alexandra A. H.; Vermunt, Jeroen K.; Hemker, Bas T.; van der Ark, L. Andries
Titel	Mokken Scale Analysis Using Hierarchical Clustering Procedures
Quelle	In: Applied Psychological Measurement, 28 (2004) 5, S.332-354 (23 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0146-6216
DOI	10.1177/0146621604265510
Schlagwörter	Measures (Individuals); Item Analysis; Item Response Theory; Item Banks; Scaling; Measurement Techniques; Multivariate Analysis; Test Construction + Suchen Sie Ihr Suchwort? Messdaten; Itemanalyse; Item-Response-Theorie; Scale construction; Skalenkonstruktion; Messtechnik; Multivariate Analyse; Testaufbau
Abstract	Mokken scale analysis (MSA) can be used to assess and build unidimensional scales from an item pool that is sensitive to multiple dimensions. These scales satisfy a set of scaling conditions, one of which follows from the model of monotone homogeneity. An important drawback of the MSA program is that the sequential item selection and scale construction procedure may not find the dominant underlying dimensionality of the responses to a set of items. The authors investigated alternative hierarchical item selection procedures and compared the performance of four hierarchical methods and the sequential clustering method in the MSA context. The results showed that hierarchical clustering methods can improve the search process of the dominant dimensionality of a data matrix. In particular, the complete linkage and scale linkage methods were promising in finding the dimensionality of the item response data from a set of items. (Author).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10