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ERIC Number: EJ945612
Record Type: Journal
Publication Date: 2011-Oct
Pages: 24
Abstractor: As Provided
Reference Count: 25
ISSN: ISSN-0033-3123
The K-INDSCAL Model for Heterogeneous Three-Way Dissimilarity Data
Bocci, Laura; Vichi, Maurizio
Psychometrika, v76 n4 p691-714 Oct 2011
A weighted Euclidean distance model for analyzing three-way dissimilarity data (stimuli by stimuli by subjects) for heterogeneous subjects is proposed. First, it is shown that INDSCAL may fail to identify a common space representative of the observed data structure in presence of heterogeneity. A new model that removes the rotational invariance of the classical multidimensional scaling problem and specifies K common homogeneous spaces is proposed. The model, called mixture INDSCAL in K classes, or briefly K-INDSCAL, still includes individual saliencies. However, the large number of parameters in K-INDSCAL may produce instability of the estimates and therefore a parsimonious model will also be discussed. The parameters of the model are estimated in a least-squares fitting context and an efficient coordinate descent algorithm is given. The usefulness of K-INDSCAL is demonstrated by both artificial and real data analyses. (Contains 3 tables and 9 figures.)
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Publication Type: Journal Articles; Reports - Descriptive
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A