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dc.contributor.authorAlberti, Gianmarco-
dc.date.accessioned2022-03-09T13:16:51Z-
dc.date.available2022-03-09T13:16:51Z-
dc.date.issued2013-
dc.identifier.citationAlberti, G. (2013). Making sense of contingency tables in archaeology: The aid of correspondence analysis to intra-site activity areas research. Journal of Data Science, 11(3), 479-499.en_GB
dc.identifier.urihttps://www.um.edu.mt/library/oar/handle/123456789/90952-
dc.description.abstractThe use of contingency tables is widespread in archaeology. Cross- tabulations are used in many different studies as a useful tool to syntheti- cally report data, and are also useful when analyst wishes to seek for latent data structures. The latter case is when Correspondence Analysis (CA) comes into play. By graphically displaying the dependence between rows and columns, CA enables the analyst to explore the data in search of a meaningful inner structure. The article aims to show the utility of CA in archaeology in general and, in particular, for the identification of areas de- voted to different activities within settlements. The application of CA to the data from a prehistoric village in north-eastern Sicily (P. Milazzese at Panarea, Aeolian Islands-Italy), taken as a case study, allows to show how CA succeeds in pinpointing different activity areas and in providing grounds to open new avenues of inquiry into other aspects of the archaeological documentation.en_GB
dc.language.isoenen_GB
dc.publisherColumbia University. Department of Statisticsen_GB
dc.rightsinfo:eu-repo/semantics/openAccessen_GB
dc.subjectCorrespondence analysis (Statistics)en_GB
dc.subjectContingency tablesen_GB
dc.subjectMultivariate analysisen_GB
dc.titleMaking sense of contingency tables in archaeology : the aid of correspondence analysis to intra-site activity areas researchen_GB
dc.typearticleen_GB
dc.rights.holderThe copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder.en_GB
dc.description.reviewedpeer-revieweden_GB
dc.publication.titleJournal of Data Scienceen_GB
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