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dc.contributor.authorSmithson, Michael
dc.contributor.authorMerkle, Edgar C.
dc.contributor.authorVerkuilen, Jay
dc.date.accessioned2012-02-21T19:53:26Z
dc.date.available2012-02-21T19:53:26Z
dc.date.issued2011en_US
dc.identifier.citationSmithson M., Merkle E.C., and Verkuilen J. 2011. "Beta regression finite mixture models of polarization and priming". Journal of Educational and Behavioral Statistics.36 (6): 804-831.en_US
dc.identifier.issn1076-9986
dc.identifier.issn1935-1054
dc.identifier.urihttp://dx.doi.org/10.3102/1076998610396893
dc.identifier.urihttp://hdl.handle.net/10057/4494
dc.descriptionClick on the DOI link below to access the article (may not be free).en_US
dc.description.abstractThis paper describes the application of finite-mixture general linear models based on the beta distribution to modeling response styles, polarization, anchoring, and priming effects in probability judgments. These models, in turn, enhance our capacity for explicitly testing models and theories regarding the aforementioned phenomena. The mixture model approach is superior in this regard to popular methods such as extremity scores, due to its incorporation of three submodels (location, dispersion, and relative composition), each of which can diagnose specific kinds of polarization and related effects. Three examples are elucidated using real data sets.en_US
dc.language.isoen_USen_US
dc.publisherAmerican Educational Research Associationen_US
dc.relation.ispartofseriesJournal of Educational and Behavioral Statistics;2011:, v.36, no.6
dc.subjectBeta distributionen_US
dc.subjectMixture modelen_US
dc.subjectPolarizationen_US
dc.subjectPrimingen_US
dc.subjectAnchorinen_US
dc.titleBeta regression finite mixture models of polarization and primingen_US
dc.typeArticleen_US
dc.description.versionPeer reviewed article
dc.rights.holderCopyright 2011 AERA


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