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dc.contributor.authorMavridis, D.en
dc.contributor.authorSalanti, G.en
dc.date.accessioned2015-11-24T17:44:51Z-
dc.date.available2015-11-24T17:44:51Z-
dc.identifier.urihttps://olympias.lib.uoi.gr/jspui/handle/123456789/14956-
dc.rightsDefault Licence-
dc.subjectBayesian methods, correlated outcomes, random effects, software, structural equation modelsen
dc.titleA practical introduction to multivariate meta-analysisen
heal.typejournalArticle-
heal.type.enJournal articleen
heal.type.elΆρθρο Περιοδικούel
heal.identifier.primary10.1177/0962280211432219-
heal.identifier.secondaryhttp://smm.sagepub.com/content/early/2012/01/23/0962280211432219.full.pdf+html-
heal.languageen-
heal.accesscampus-
heal.recordProviderΠανεπιστήμιο Ιωαννίνων. Σχολή Επιστημών Αγωγής. Παιδαγωγικό Τμήμα Δημοτικής Εκπαίδευσηςel
heal.publicationDate2012-
heal.abstractMultivariate meta-analysis is becoming increasingly popular and official routines or self-programmed functions have been included in many statistical software. In this article, we review the statistical methods and the related software for multivariate meta-analysis. Emphasis is placed on Bayesian methods using Markov chain Monte Carlo, and codes in WinBUGS are provided. The various modelfitting options are illustrated in two examples and specific guidance is provided on how to run a multivariate meta-analysis using various software packages.en
heal.journalNameStatistical Methods in Medical Researchen
heal.journalTypepeer-reviewed-
heal.fullTextAvailabilityTRUE-
Appears in Collections:Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά)

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