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Mokken Scale Analysis Using Hierarchical Clustering Procedures

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Title: Mokken Scale Analysis Using Hierarchical Clustering Procedures
Language: English
Authors: van Abswoude, Alexandra A. H., Vermunt, Jeroen K., Hemker, Bas T., van der Ark, L. Andries
Source: Applied Psychological Measurement. 2004 28(5):332-354.
Availability: SAGE Publications, 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243 (Toll Free); Fax: 800-583-2665 (Toll Free).
Peer Reviewed: Y
Page Count: 23
Publication Date: 2004
Document Type: Journal Articles
Reports - Evaluative
Descriptors: Measures (Individuals), Item Analysis, Item Response Theory, Item Banks, Scaling, Measurement Techniques, Multivariate Analysis, Test Construction
DOI: 10.1177/0146621604265510
ISSN: 0146-6216
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.
Abstractor: Author
Entry Date: 2006
Accession Number: EJ727363
Database: ERIC
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  Data: <searchLink fieldCode="SO" term="%22Applied+Psychological+Measurement%22"><i>Applied Psychological Measurement</i></searchLink>. 2004 28(5):332-354.
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  Data: 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.
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