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- aggregation classification "A1".
- aggregation creator B840615.
- aggregation creator B840616.
- aggregation creator B840617.
- aggregation creator person.
- aggregation date "2013".
- aggregation format "application/pdf".
- aggregation hasFormat 5683161.bibtex.
- aggregation hasFormat 5683161.csv.
- aggregation hasFormat 5683161.dc.
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- aggregation isPartOf urn:issn:1554-351X.
- aggregation language "eng".
- aggregation rights "I have transferred the copyright for this publication to the publisher".
- aggregation subject "Social Sciences".
- aggregation title "SCA with rotation to distinguish common and distinctive information in linked data".
- aggregation abstract "Often data are collected that consist of different blocks that all contain information about the same entities (e. g., items, persons, or situations). In order to unveil both information that is common to all data blocks and information that is distinctive for one or a few of them, an integrated analysis of the whole of all data blocks may be most useful. Interesting classes of methods for such an approach are simultaneous-component and multigroup factor analysis methods. These methods yield dimensions underlying the data at hand. Unfortunately, however, in the results from such analyses, common and distinctive types of information are mixed up. This article proposes a novel method to disentangle the two kinds of information, by making use of the rotational freedom of component and factor models. We illustrate this method with data from a cross-cultural study of emotions.".
- aggregation authorList BK1217088.
- aggregation endPage "833".
- aggregation issue "3".
- aggregation startPage "822".
- aggregation volume "45".
- aggregation aggregates 5683176.
- aggregation isDescribedBy 5683161.
- aggregation similarTo s13428-012-0295-9.
- aggregation similarTo LU-5683161.