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dc.contributor.authorVakhitova, Zarinaen_US
dc.contributor.authorAlston-Knox, Clairen_US
dc.date.accessioned2019-05-29T12:39:45Z
dc.date.available2019-05-29T12:39:45Z
dc.date.issued2018en_US
dc.identifier.issn1932-6203en_US
dc.identifier.doi10.1371/journal.pone.0205076en_US
dc.identifier.urihttp://hdl.handle.net/10072/381412
dc.description.abstractIn the context of generalized linear models (GLMs), interactions are automatically induced on the natural scale of the data. The conventional approach to measuring effects in GLMs based on significance testing (e.g. the Wald test or using deviance to assess model fit) is not always appropriate. The objective of this paper is to demonstrate the limitations of these conventional approaches and to explore alternative strategies for determining the importance of effects. The paper compares four approaches to determining the importance of effects in the GLM using 1) the Wald statistic, 2) change in deviance (model fitting criteria), 3) Bayesian GLM using vaguely informative priors and 4) Bayesian Model Averaging analysis. The main points in this paper are illustrated using an example study, which examines the risk factors for cyber abuse victimization, and are further examined using a simulation study. Analysis of our example dataset shows that, in terms of a logistic GLM, the conventional methods using the Wald test and the change in deviance can produce results that are difficult to interpret; Bayesian analysis of GLM is a suitable alternative, which is enhanced with prior knowledge about the direction of the effects; and Bayesian Model Averaging (BMA) is especially suited for new areas of research, particularly in the absence of theory. We recommend that social scientists consider including BMA in their standard toolbox for analysis of GLMs.en_US
dc.description.peerreviewedYesen_US
dc.languageEnglishen_US
dc.publisherPublic Library of Sciencesen_US
dc.publisher.placeUnited Statesen_US
dc.relation.ispartofpagefrom1en_US
dc.relation.ispartofpageto32en_US
dc.relation.ispartofissue11en_US
dc.relation.ispartofjournalPLoS Oneen_US
dc.relation.ispartofvolume13en_US
dc.subject.fieldofresearchStatistical Theoryen_US
dc.subject.fieldofresearchMultidisciplinaryen_US
dc.subject.fieldofresearchcode010405en_US
dc.subject.fieldofresearchcodeMDen_US
dc.titleNon-significant p-values? Strategies to understand and better determine the importance of effects and interactions in logistic regressionen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Articlesen_US
dc.type.codeC - Journal Articlesen_US
dcterms.licensehttp://creativecommons.org/licenses/by/4.0/en_US
dc.description.versionPublisheden_US
gro.facultyArts, Education & Law Group, School of Criminology and Criminal Justiceen_US
gro.rights.copyright© 2018 Vakhitova, Alston-Knox. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.en_US
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