Data-Driven, Statistical Learning Method for Inductive Confirmation of Structural Models

dc.contributor.author Maass, Wolfgang
dc.contributor.author Shcherbatyi, Iaroslav
dc.date.accessioned 2016-12-29T02:06:45Z
dc.date.available 2016-12-29T02:06:45Z
dc.date.issued 2017-01-04
dc.description.abstract Automatic extraction of structural models interferes with the deductive research method in information systems research. Nonetheless it is tempting to use a statistical learning method for assessing meaningful relations between structural variables given the underlying measurement model. In this paper, we discuss the epistemological background for this method and describe its general structure. Thereafter this method is applied in a mode of inductive confirmation to an existing data set that has been used for evaluating a deductively derived structural model. In this study, a range of machine learning model classes is used for statistical learning and results are compared with the original model.
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2017.687
dc.identifier.isbn 978-0-9981331-0-2
dc.identifier.uri http://hdl.handle.net/10125/41850
dc.language.iso eng
dc.relation.ispartof Proceedings of the 50th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject hybrid mode of research
dc.subject inductive confirmation
dc.subject machine learning
dc.subject structural equation models
dc.title Data-Driven, Statistical Learning Method for Inductive Confirmation of Structural Models
dc.type Conference Paper
dc.type.dcmi Text
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