01441nas a2200157 4500008003900000245006700039210006700106300001200173490000700185520094700192100002901139700002301168700001901191700002001210856005301230 2004 d00aMokken Scale Analysis Using Hierarchical Clustering Procedures0 aMokken Scale Analysis Using Hierarchical Clustering Procedures a332-3540 v283 a
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.
1 aAbswoude, Alexandra, A H1 aVermunt, Jeroen, K1 aHemker, Bas, T1 aArk, Andries, L uhttp://apm.sagepub.com/content/28/5/332.abstract