Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/41850

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

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Item Summary

Title: Data-Driven, Statistical Learning Method for Inductive Confirmation of Structural Models
Authors: Maass, Wolfgang
Shcherbatyi, Iaroslav
Keywords: hybrid mode of research
inductive confirmation
machine learning
structural equation models
Issue Date: 04 Jan 2017
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.
Pages/Duration: 10 pages
URI/DOI: http://hdl.handle.net/10125/41850
ISBN: 978-0-9981331-0-2
DOI: 10.24251/HICSS.2017.687
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
Appears in Collections:Theory and Information Systems Minitrack



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