Multidimensional scaling
Identifieur interne : 001841 ( Pmc/Curation ); précédent : 001840; suivant : 001842Multidimensional scaling
Auteurs : Michael C. Hout ; Megan H. Papesh ; Stephen D. GoldingerSource :
- Wiley interdisciplinary reviews. Cognitive science [ 1939-5078 ] ; 2012.
Abstract
The concept of similarity, or a sense of "sameness" among things, is pivotal to theories in the cognitive sciences and beyond. Similarity, however, is a difficult thing to measure. Multidimensional scaling (MDS) is a tool by which researchers can obtain quantitative estimates of similarity among groups of items. More formally, MDS refers to a set of statistical techniques that are used to reduce the complexity of a data set, permitting visual appreciation of the underlying relational structures contained therein. The current paper provides an overview of MDS. We discuss key aspects of performing this technique, such as methods that can be used to collect similarity estimates, analytic techniques for treating proximity data, and various concerns regarding interpretation of the MDS output. MDS analyses of two novel data sets are also included, highlighting in step-by-step fashion how MDS is performed, and key issues that may arise during analysis.
Url:
DOI: 10.1002/wcs.1203
PubMed: 23359318
PubMed Central: 3555222
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Multidimensional scaling</title><author><name sortKey="Hout, Michael C" sort="Hout, Michael C" uniqKey="Hout M" first="Michael C." last="Hout">Michael C. Hout</name></author><author><name sortKey="Papesh, Megan H" sort="Papesh, Megan H" uniqKey="Papesh M" first="Megan H." last="Papesh">Megan H. Papesh</name></author><author><name sortKey="Goldinger, Stephen D" sort="Goldinger, Stephen D" uniqKey="Goldinger S" first="Stephen D." last="Goldinger">Stephen D. Goldinger</name></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">23359318</idno><idno type="pmc">3555222</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3555222</idno><idno type="RBID">PMC:3555222</idno><idno type="doi">10.1002/wcs.1203</idno><date when="2012">2012</date><idno type="wicri:Area/Pmc/Corpus">001841</idno><idno type="wicri:Area/Pmc/Curation">001841</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Multidimensional scaling</title><author><name sortKey="Hout, Michael C" sort="Hout, Michael C" uniqKey="Hout M" first="Michael C." last="Hout">Michael C. Hout</name></author><author><name sortKey="Papesh, Megan H" sort="Papesh, Megan H" uniqKey="Papesh M" first="Megan H." last="Papesh">Megan H. Papesh</name></author><author><name sortKey="Goldinger, Stephen D" sort="Goldinger, Stephen D" uniqKey="Goldinger S" first="Stephen D." last="Goldinger">Stephen D. Goldinger</name></author></analytic><series><title level="j">Wiley interdisciplinary reviews. Cognitive science</title><idno type="ISSN">1939-5078</idno><idno type="eISSN">1939-5086</idno><imprint><date when="2012">2012</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p id="P1">The concept of similarity, or a sense of "sameness" among things, is pivotal to theories in the cognitive sciences and beyond. Similarity, however, is a difficult thing to measure. Multidimensional scaling (MDS) is a tool by which researchers can obtain quantitative estimates of similarity among groups of items. More formally, MDS refers to a set of statistical techniques that are used to reduce the complexity of a data set, permitting visual appreciation of the underlying relational structures contained therein. The current paper provides an overview of MDS. We discuss key aspects of performing this technique, such as methods that can be used to collect similarity estimates, analytic techniques for treating proximity data, and various concerns regarding interpretation of the MDS output. MDS analyses of two novel data sets are also included, highlighting in step-by-step fashion how MDS is performed, and key issues that may arise during analysis.</p></div></front></TEI><pmc article-type="research-article"><pmc-comment>The publisher of this article does not allow downloading of the full text in XML form.</pmc-comment>
<pmc-dir>properties manuscript</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-journal-id">101524169</journal-id><journal-id journal-id-type="pubmed-jr-id">38621</journal-id><journal-id journal-id-type="nlm-ta">Wiley Interdiscip Rev Cogn Sci</journal-id><journal-id journal-id-type="iso-abbrev">Wiley Interdiscip Rev Cogn Sci</journal-id><journal-title-group><journal-title>Wiley interdisciplinary reviews. Cognitive science</journal-title></journal-title-group><issn pub-type="ppub">1939-5078</issn><issn pub-type="epub">1939-5086</issn></journal-meta><article-meta><article-id pub-id-type="pmid">23359318</article-id><article-id pub-id-type="pmc">3555222</article-id><article-id pub-id-type="doi">10.1002/wcs.1203</article-id><article-id pub-id-type="manuscript">NIHMS407684</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Multidimensional scaling</article-title></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><name><surname>Hout</surname><given-names>Michael C.</given-names></name><email>michael.hout@asu.edu</email><aff id="A1">Arizona State University</aff></contrib><contrib contrib-type="author"><name><surname>Papesh</surname><given-names>Megan H.</given-names></name><aff id="A2">Louisiana State University</aff></contrib><contrib contrib-type="author"><name><surname>Goldinger</surname><given-names>Stephen D.</given-names></name><aff id="A3">Arizona State University</aff></contrib></contrib-group><pub-date pub-type="nihms-submitted"><day>14</day><month>9</month><year>2012</year></pub-date><pub-date pub-type="epub"><day>8</day><month>10</month><year>2012</year></pub-date><pub-date pub-type="ppub"><day>1</day><month>1</month><year>2013</year></pub-date><pub-date pub-type="pmc-release"><day>01</day><month>1</month><year>2014</year></pub-date><volume>4</volume><issue>1</issue><fpage>93</fpage><lpage>103</lpage><abstract><p id="P1">The concept of similarity, or a sense of "sameness" among things, is pivotal to theories in the cognitive sciences and beyond. Similarity, however, is a difficult thing to measure. Multidimensional scaling (MDS) is a tool by which researchers can obtain quantitative estimates of similarity among groups of items. More formally, MDS refers to a set of statistical techniques that are used to reduce the complexity of a data set, permitting visual appreciation of the underlying relational structures contained therein. The current paper provides an overview of MDS. We discuss key aspects of performing this technique, such as methods that can be used to collect similarity estimates, analytic techniques for treating proximity data, and various concerns regarding interpretation of the MDS output. MDS analyses of two novel data sets are also included, highlighting in step-by-step fashion how MDS is performed, and key issues that may arise during analysis.</p></abstract><funding-group><award-group><funding-source country="United States">National Institute on Deafness and Other Communication Disorders : NIDCD</funding-source><award-id>R01 DC004535 || DC</award-id></award-group></funding-group></article-meta></front></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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