Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/13313
DC FieldValueLanguage
dc.contributor.authorKarapanos, Evangelos-
dc.date.accessioned2019-02-13T07:02:52Z-
dc.date.available2019-02-13T07:02:52Z-
dc.date.issued2013-
dc.identifier.isbn978-3-642-31000-3-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/13313-
dc.description.abstractThis chapter presents a second Multi-Dimensional Scaling procedure that aims at identifying diverse views even within single individuals. The technique is applied on an existing dataset (Heidecker and Hassenzahl, 2007). It is illustrated that the-traditional-averaging analysis provides insight to only 1/6th of the total number of attributes in the example dataset. The proposed approach accounts for more than double the information obtained from the average model, and provides richer and semantically more diverse views on the set of stimuli.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.rights© 2013 Springer-Verlag Berlin Heidelberg.en_US
dc.subjectAkaike Information Criterionen_US
dc.subjectAverage Modelen_US
dc.subjectAttribute Categoryen_US
dc.subjectAverage Analysisen_US
dc.titleAnalyzing personal attribute judgmentsen_US
dc.typeBook Chapteren_US
dc.collaborationMadeira Interactive Technologies Instituteen_US
dc.subject.categoryComputer and Information Sciencesen_US
dc.countryPortugalen_US
dc.subject.fieldNatural Sciencesen_US
dc.publicationPeer Revieweden_US
dc.identifier.doi10.1007/978-3-642-31000-3_3-
cut.common.academicyear2013-2014en_US
item.cerifentitytypePublications-
item.openairetypebookPart-
item.fulltextNo Fulltext-
item.grantfulltextnone-
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_3248-
crisitem.author.deptDepartment of Communication and Internet Studies-
crisitem.author.facultyFaculty of Communication and Media Studies-
crisitem.author.orcid0000-0001-5910-4996-
crisitem.author.parentorgFaculty of Communication and Media Studies-
Appears in Collections:Κεφάλαια βιβλίων/Book chapters
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