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dc.contributor.authorGelman, Andrewen_US
dc.contributor.authorHill, Jenniferen_US
dc.contributor.authorYajima, Masanaoen_US
dc.date.accessioned2018-03-29T17:52:20Z
dc.date.available2018-03-29T17:52:20Z
dc.date.issued2012
dc.identifierhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000323946100004&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=6e74115fe3da270499c3d65c9b17d654
dc.identifier.citationAndrew Gelman, Jennifer Hill, Masanao Yajima. 2012. "Why We (Usually) Don't Have to Worry About Multiple Comparisons." JOURNAL OF RESEARCH ON EDUCATIONAL EFFECTIVENESS, Volume 5, Issue 2, pp. 189 - 211.
dc.identifier.issn1934-5747
dc.identifier.urihttps://hdl.handle.net/2144/27897
dc.descriptionThis is an Accepted Manuscript of an article published by Taylor & Francis Group in Journal Of Research On Educational Effectiveness on 04/03/2012, available online: https://doi.org/10.1080/19345747.2011.618213en_US
dc.description.abstractApplied researchers often find themselves making statistical inferences in settings that would seem to require multiple comparisons adjustments. We challenge the Type I error paradigm that underlies these corrections. Moreover we posit that the problem of multiple comparisons can disappear entirely when viewed from a hierarchical Bayesian perspective. We propose building multilevel models in the settings where multiple comparisons arise. Multilevel models perform partial pooling (shifting estimates toward each other), whereas classical procedures typically keep the centers of intervals stationary, adjusting for multiple comparisons by making the intervals wider (or, equivalently, adjusting the p values corresponding to intervals of fixed width). Thus, multilevel models address the multiple comparisons problem and also yield more efficient estimates, especially in settings with low group-level variation, which is where multiple comparisons are a particular concern.en_US
dc.format.extent189 - 211en_US
dc.relation.ispartofJOURNAL OF RESEARCH ON EDUCATIONAL EFFECTIVENESS
dc.subjectBayesian inferenceen_US
dc.subjectHierarchical modelingen_US
dc.subjectMultiple comparisonsen_US
dc.subjectType S erroren_US
dc.subjectStatistical significanceen_US
dc.titleWhy we (usually) don't have to worry about multiple comparisonsen_US
dc.typeArticleen_US
dc.identifier.doi10.1080/19345747.2011.618213
pubs.elements-sourcewos-liteen_US
pubs.notesEmbargo: Not knownen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, College of Arts & Sciencesen_US
pubs.organisational-groupBoston University, College of Arts & Sciences, Department of Mathematics & Statisticsen_US
pubs.publication-statusPublisheden_US


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