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Multidimensional unfolding methods are widely used for visualizing item response data. Such methods project respondents and items simultaneously onto a low-dimensional Euclidian space, in which respondents and items are represented by ideal points, with person-person, item-item, and person-item similarities being captured by the Euclidian distances...
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Context 1
... precisely, MDS maps a set of variables onto a low dimensional space, based on data measuring the similarity between variables. As pointed out in Chapter 14, Borg and Groenen (2005), MDU can be viewed as a special case of MDS, where the set of variables in MDS composes of both the respondents and items and the item response data (y ij ) N ×J are regarded as measures of similarity between the respondents and the items, while the similarities within the two sets (i.e., respondents and items) are structurally missing; see Figure 3 for an illustration that is a reproduction of Figure 14.1 of Borg and Groenen (2005). Little statistical theory has been developed for the recovery of configuration based on MDS models. ...
Context 2
... precisely, MDS maps a set of variables onto a low dimensional space, based on data measuring the similarity between variables. As pointed out in Chapter 14, Borg and Groenen (2005), MDU can be viewed as a special case of MDS, where the set of variables in MDS composes of both the respondents and items and the item response data (y ij ) N ×J are regarded as measures of similarity between the respondents and the items, while the similarities within the two sets (i.e., respondents and items) are structurally missing; see Figure 3 for an illustration that is a reproduction of Figure 14.1 of Borg and Groenen (2005). Little statistical theory has been developed for the recovery of configuration based on MDS models. ...
