Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/164284
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dc.contributor.authorBloome, Deirdreen_US
dc.contributor.authorAng, Shannonen_US
dc.date.accessioned2023-01-13T02:50:39Z-
dc.date.available2023-01-13T02:50:39Z-
dc.date.issued2022-
dc.identifier.citationBloome, D. & Ang, S. (2022). Is the effect larger in group A or B? It depends: understanding results from nonlinear probability models. Demography, 59(4), 1459-1488. https://dx.doi.org/10.1215/00703370-10109444en_US
dc.identifier.issn0070-3370en_US
dc.identifier.urihttps://hdl.handle.net/10356/164284-
dc.description.abstractDemographers and other social scientists often study effect heterogeneity (defined here as differences in outcome-predictor associations across groups defined by the values of a third variable) to understand how inequalities evolve between groups or how groups differentially benefit from treatments. Yet answering the question "Is the effect larger in group A or group B?" is surprisingly difficult. In fact, the answer sometimes reverses across scales. For example, researchers might conclude that the effect of education on mortality is larger among women than among men if they quantify education's effect on an odds-ratio scale, but their conclusion might flip (to indicate a larger effect among men) if they instead quantify education's effect on a percentage-point scale. We illuminate this flipped-signs phenomenon in the context of nonlinear probability models, which were used in about one third of articles published in Demography in 2018-2019. Although methodologists are aware that flipped signs can occur, applied researchers have not integrated this insight into their work. We provide formal inequalities that researchers can use to easily determine if flipped signs are a problem in their own applications. We also share practical tips to help researchers handle flipped signs and, thus, generate clear and substantively correct descriptions of effect heterogeneity. Our findings advance researchers' ability to accurately characterize population variation.en_US
dc.language.isoenen_US
dc.relation.ispartofDemographyen_US
dc.rights© 2022 The Authors. This is an open access article distributed under the terms of a Creative Commons license (CC BY-NC-ND 4.0).en_US
dc.subjectSocial sciences::Sociologyen_US
dc.titleIs the effect larger in group A or B? It depends: understanding results from nonlinear probability modelsen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Social Sciencesen_US
dc.identifier.doi10.1215/00703370-10109444-
dc.description.versionPublished versionen_US
dc.identifier.pmid35894791-
dc.identifier.scopus2-s2.0-85135525980-
dc.identifier.issue4en_US
dc.identifier.volume59en_US
dc.identifier.spage1459en_US
dc.identifier.epage1488en_US
dc.subject.keywordsInteractionen_US
dc.subject.keywordsModerationen_US
dc.description.acknowledgementWe gratefully acknowledge support from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (research grant P01HD087155 and center grant P2CHD041028).en_US
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