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Van Deun, Katrijn; Heiser, Willem J.; Delbeke, Luc – Multivariate Behavioral Research, 2007
A multidimensional unfolding technique that is not prone to degenerate solutions and is based on multidimensional scaling of a complete data matrix is proposed: distance information about the unfolding data and about the distances both among judges and among objects is included in the complete matrix. The latter information is derived from the…
Descriptors: Multidimensional Scaling, Correlation, Simulation, Computer Software
Peer reviewedMeara, Kevin; Robin, Frederic; Sireci, Stephen G. – Multivariate Behavioral Research, 2000
Investigated the usefulness of multidimensional scaling (MDS) for assessing the dimensionality of dichotomous test data. Focused on two MDS proximity measures, one based on the PC statistic (T. Chen and M. Davidson, 1996) and other, on interitem Euclidean distances. Simulation results show that both MDS procedures correctly identify…
Descriptors: Correlation, Multidimensional Scaling, Simulation, Test Items
Peer reviewedKim, Chulwan; Rangaswamy, Arvind; DeSarbo, Wayne S. – Multivariate Behavioral Research, 1999
Presents an approach to multidimensional unfolding that reduces the occurrence of degenerate solutions and conducts a Monte Carlo study to demonstrate the superiority of the new method to the ALSCAL and KYST nonmetric procedures for student preference data. (SLD)
Descriptors: Monte Carlo Methods, Multidimensional Scaling, Problem Solving, Simulation

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