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2021-01-25Zeitschriftenartikel DOI: 10.3389/fpsyg.2020.611267
Prior Specification for More Stable Bayesian Estimation of Multilevel Latent Variable Models in Small Samples: A Comparative Investigation of Two Different Approaches
dc.contributor.authorZitzmann, Steffen
dc.contributor.authorHelm, Christoph
dc.contributor.authorHecht, Martin
dc.date.accessioned2021-04-29T09:03:31Z
dc.date.available2021-04-29T09:03:31Z
dc.date.issued2021-01-25none
dc.date.updated2021-02-08T05:46:13Z
dc.identifier.urihttp://edoc.hu-berlin.de/18452/23516
dc.description.abstractBayesian approaches for estimating multilevel latent variable models can be beneficial in small samples. Prior distributions can be used to overcome small sample problems, for example, when priors that increase the accuracy of estimation are chosen. This article discusses two different but not mutually exclusive approaches for specifying priors. Both approaches aim at stabilizing estimators in such a way that the Mean Squared Error (MSE) of the estimator of the between-group slope will be small. In the first approach, the MSE is decreased by specifying a slightly informative prior for the group-level variance of the predictor variable, whereas in the second approach, the decrease is achieved directly by using a slightly informative prior for the slope. Mathematical and graphical inspections suggest that both approaches can be effective for reducing the MSE in small samples, thus rendering them attractive in these situations. The article also discusses how these approaches can be implemented in Mplus.eng
dc.language.isoengnone
dc.publisherHumboldt-Universität zu Berlin
dc.rights(CC BY 4.0) Attribution 4.0 Internationalger
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectBayesian estimationeng
dc.subjectMarkov chain Monte Carloeng
dc.subjectmultilevel modelingeng
dc.subjectstructural equation modelingeng
dc.subjectsmall sampleeng
dc.subject.ddc150 Psychologienone
dc.titlePrior Specification for More Stable Bayesian Estimation of Multilevel Latent Variable Models in Small Samples: A Comparative Investigation of Two Different Approachesnone
dc.typearticle
dc.identifier.urnurn:nbn:de:kobv:11-110-18452/23516-5
dc.identifier.doi10.3389/fpsyg.2020.611267none
dc.identifier.doihttp://dx.doi.org/10.18452/22846
dc.type.versionpublishedVersionnone
local.edoc.container-titleFrontiers in psychologynone
local.edoc.pages11none
local.edoc.type-nameZeitschriftenartikel
local.edoc.institutionLebenswissenschaftliche Fakultätnone
local.edoc.container-typeperiodical
local.edoc.container-type-nameZeitschrift
local.edoc.container-publisher-nameFrontiers Research Foundationnone
local.edoc.container-publisher-placeLausannenone
local.edoc.container-volume11none
dc.description.versionPeer Reviewednone
local.edoc.container-articlenumber611267none
dc.identifier.eissn1664-1078
local.edoc.affiliationZitzmann, Steffen: Hector Research Institute of Education Sciences and Psychology, University of Tübingen, Tübingen, Germanynone
local.edoc.affiliationHelm, Christoph: Institute for the Management and Economics of Education, University of Teacher Education Zug, Zug, Switzerlandnone
local.edoc.affiliationHecht, Martin: Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germanynone

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