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dc.contributor.authorBallard, T.
dc.contributor.authorPalada, H.
dc.contributor.authorGriffin, Mark
dc.contributor.authorNeal, A.
dc.date.accessioned2020-03-26T05:53:52Z
dc.date.available2020-03-26T05:53:52Z
dc.date.issued2019
dc.identifier.citationBallard, T. and Palada, H. and Griffin, M. and Neal, A. 2019. An Integrated Approach to Testing Dynamic, Multilevel Theory: Using Computational Models to Connect Theory, Model, and Data. Organizational Research Methods. : UNSP 1094428119881209.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/78407
dc.identifier.doi10.1177/1094428119881209
dc.description.abstract

© The Author(s) 2019. Some of the most influential theories in organizational sciences explicitly describe a dynamic, multilevel process. Yet the inherent complexity of such theories makes them difficult to test. These theories often describe multiple subprocesses that interact reciprocally over time at different levels of analysis and over different time scales. Computational (i.e., mathematical) modeling is increasingly advocated as a method for developing and testing theories of this type. In organizational sciences, however, efforts that have been made to test models empirically are often indirect. We argue that the full potential of computational modeling as a tool for testing dynamic, multilevel theory is yet to be realized. In this article, we demonstrate an approach to testing dynamic, multilevel theory using computational modeling. The approach uses simulations to generate model predictions and Bayesian parameter estimation to fit models to empirical data and facilitate model comparisons. This approach enables a direct integration between theory, model, and data that we believe enables a more rigorous test of theory.

dc.languageEnglish
dc.publisherSAGE PUBLICATIONS INC
dc.subjectSocial Sciences
dc.subjectPsychology, Applied
dc.subjectManagement
dc.subjectPsychology
dc.subjectBusiness & Economics
dc.subjectdynamic theory
dc.subjectcomputational modeling
dc.subjectmultilevel research
dc.subjectBayesian parameter estimation
dc.subjectself-regulation
dc.subjectORGANIZATIONAL ROUTINES
dc.subjectINDIVIDUAL-DIFFERENCES
dc.subjectSELF-REGULATION
dc.subjectGOAL REVISION
dc.subjectFORMAL MODEL
dc.subjectPERFORMANCE
dc.subjectSIMULATION
dc.subjectMOTIVATION
dc.subjectFRAMEWORK
dc.subjectTIME
dc.titleAn Integrated Approach to Testing Dynamic, Multilevel Theory: Using Computational Models to Connect Theory, Model, and Data
dc.typeJournal Article
dcterms.source.issn1094-4281
dcterms.source.titleOrganizational Research Methods
dc.date.updated2020-03-26T05:53:51Z
curtin.departmentFuture of Work Institute
curtin.accessStatusIn process
curtin.facultyFaculty of Business and Law
curtin.identifier.article-numberUNSP 1094428119881209
dcterms.source.eissn1552-7425


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