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dc.contributor.authorBaumgartner, Michael Clemens
dc.contributor.authorFalk, Christoph
dc.date.accessioned2021-10-04T09:13:15Z
dc.date.available2021-10-04T09:13:15Z
dc.date.created2021-10-01T23:24:09Z
dc.date.issued2021
dc.identifier.issn0027-3171
dc.identifier.urihttps://hdl.handle.net/11250/2787408
dc.description.abstractConfigurational comparative methods (CCMs) and logic regression methods (LRMs) are two families of exploratory methods that employ very different techniques to analyze data generated by causal structures featuring conjunctural causation and equifinality. Aiming for the same by different means carries a substantive synergy potential, which, however, remains untapped so far because representatives of the two frameworks know little of each other. The purpose of this article is to change that. We first level the field for readers from both backgrounds by providing brief introductions to the basic ideas behind CCMs and LRMs. Then, we carve out the strengths and weaknesses of the two method families by benchmarking their performance when applied to binary data under a variety of different discovery contexts. It turns out that CCMs and LRMs have complementary strengths and weaknesses. This creates various promising avenues for cross-validation.en_US
dc.language.isoengen_US
dc.publisherTaylor & Francisen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleConfigurational Causal Modeling and Logic Regressionen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionpublishedVersionen_US
dc.rights.holderCopyright 2021 the authorsen_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1
dc.identifier.doi10.1080/00273171.2021.1971510
dc.identifier.cristin1942518
dc.source.journalMultivariate Behavioral Researchen_US
dc.identifier.citationMultivariate Behavioral Research. 2021.en_US


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Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal