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

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dc.contributor.authorMaass, Wolfgang
dc.contributor.authorShcherbatyi, Iaroslav
dc.date.accessioned2016-12-29T02:06:45Z
dc.date.available2016-12-29T02:06:45Z
dc.date.issued2017-01-04
dc.description.abstractAutomatic 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.extent10 pages
dc.identifier.doi10.24251/HICSS.2017.687
dc.identifier.isbn978-0-9981331-0-2
dc.identifier.urihttp://hdl.handle.net/10125/41850
dc.language.isoeng
dc.relation.ispartofProceedings of the 50th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjecthybrid mode of research
dc.subjectinductive confirmation
dc.subjectmachine learning
dc.subjectstructural equation models
dc.titleData-Driven, Statistical Learning Method for Inductive Confirmation of Structural Models
dc.typeConference Paper
dc.type.dcmiText

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