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Estimating Formative Measurement Models in IS Research – Analysis of the Past and Recommendations for the Future

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Rönkkö, Mikko
dc.contributor.author Evermann, Joerg
dc.contributor.author Aguirre-Urreta, Miguel I.
dc.date.accessioned 2016-05-04T09:01:14Z
dc.date.available 2016-05-04T09:01:14Z
dc.date.issued 2016
dc.identifier.citation Rönkkö, Mikko & Evermann, Joerg & Aguirre-Urreta, Miguel I. 2016. Estimating Formative Measurement Models in IS Research – Analysis of the Past and Recommendations for the Future. Unpublished working paper. 56 pages + app. 772 pages.
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/20261
dc.description.abstract While debates on the appropriateness of formative measurement within structural equation models continue, such models are frequently found in IS research. IS researchers faced with such a model must identify the best method to estimate the model parameters, and have at their disposal covariance-based structural equation modeling (CBSEM), Partial Least Squares path modeling (PLS), and regression with summed scales, among other techniques.While all these methods can estimate models with formatively-specified latent variables, IS researchers frequently cite the presence of formative measurement as the reason for choosing PLS for model estimation over alternatives. Intuitively, a composite-based method such as PLS would appear to have an advantage in this particular scenario. In fact, some PLS researchers argue that PLS should only be used for such models. However, there is a dearth of empirical studies showing whether such an advantage does indeed exist.In this research, we discuss the statistical problems posed by models that include formatively-specified latent variables, and present a large-scale simulation study to investigate the relative performance of different estimation methods when faced with formative measurement, using models from studies published in MIS Quarterly. Based on our simulation results, we present recommendations for IS researchers interested in the estimation of models that include formatively-specified latent variables. en
dc.format.extent 56 + app. 772
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Aalto University en
dc.publisher Aalto-yliopisto fi
dc.relation.ispartof Unpublished working paper en
dc.subject.other Mathematics en
dc.title Estimating Formative Measurement Models in IS Research – Analysis of the Past and Recommendations for the Future en
dc.type J Muu elektroninen julkaisu fi
dc.contributor.school Perustieteiden korkeakoulu fi
dc.contributor.school School of Science en
dc.contributor.department Tuotantotalouden laitos fi
dc.contributor.department Department of Industrial Engineering and Management en
dc.subject.keyword Formative measurement en
dc.subject.keyword structural equation modeling en
dc.subject.keyword estimation en
dc.subject.keyword maximum likelihood en
dc.subject.keyword partial least squares en
dc.identifier.urn URN:NBN:fi:aalto-201605031907
dc.type.dcmitype text en

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