Please use this identifier to cite or link to this item:
Data-Driven, Statistical Learning Method for Inductive Confirmation of Structural Models
|Title:||Data-Driven, Statistical Learning Method for Inductive Confirmation of Structural Models|
|Keywords:||hybrid mode of research|
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.|
|Rights:||Attribution-NonCommercial-NoDerivatives 4.0 International|
|Appears in Collections:||Theory and Information Systems Minitrack|
Please contact email@example.com if you need this content in an alternative format.
Items in ScholarSpace are protected by copyright, with all rights reserved, unless otherwise indicated.