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dc.contributor.authorMäkikangas, Anne
dc.contributor.authorTolvanen, Asko
dc.contributor.authorAunola, Kaisa
dc.contributor.authorFeldt, Taru
dc.contributor.authorMauno, Saija
dc.contributor.authorKinnunen, Ulla
dc.date.accessioned2018-12-19T12:29:31Z
dc.date.available2019-02-24T22:35:37Z
dc.date.issued2018
dc.identifier.citationMäkikangas, A., Tolvanen, A., Aunola, K., Feldt, T., Mauno, S., & Kinnunen, U. (2018). Multilevel Latent Profile Analysis With Covariates : Identifying Job Characteristics Profiles in Hierarchical Data as an Example. <i>Organizational Research Methods</i>, <i>21</i>(4), 931-954. <a href="https://doi.org/10.1177/1094428118760690" target="_blank">https://doi.org/10.1177/1094428118760690</a>
dc.identifier.otherCONVID_27911491
dc.identifier.otherTUTKAID_76863
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/60696
dc.description.abstractLatent profile analysis (LPA) is a person-centered method commonly used in organizational research to identify homogeneous subpopulations of employees within a heterogeneous population. However, in the case of nested data structures, such as employees nested in work departments, multilevel techniques are needed. Multilevel LPA (MLPA) enables adequate modeling of subpopulations in hierarchical data sets. MLPA enables investigation of variability in the proportions of Level 1 profiles across Level 2 units, and of Level 2 latent classes based on the proportions of Level 1 latent profiles and Level 1 ratings, and the extent to which covariates drawn from the different hierarchical levels of the data affect the probability of a membership of a particular profile. We demonstrate the use of MLPA by investigating job characteristics profiles based on the job-demand-control-support (JDCS) model using data from 1,958 university employees clustered in 78 work departments. The implications of the results for organizational research are discussed, together with several issues related to the potential of MLPA for wider application.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherSage Publications
dc.relation.ispartofseriesOrganizational Research Methods
dc.rightsIn Copyright
dc.subject.othermultilevel latent profile analysis
dc.subject.otherclustered data
dc.subject.otherhierarchical structure
dc.subject.otherjob demand-control-support model
dc.titleMultilevel Latent Profile Analysis With Covariates : Identifying Job Characteristics Profiles in Hierarchical Data as an Example
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-201812185201
dc.contributor.laitosPsykologian laitosfi
dc.contributor.laitosDepartment of Psychologyen
dc.contributor.oppiainePsykologiafi
dc.contributor.oppiainePsychologyen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.date.updated2018-12-18T13:15:09Z
dc.description.reviewstatuspeerReviewed
dc.format.pagerange931-954
dc.relation.issn1094-4281
dc.relation.numberinseries4
dc.relation.volume21
dc.type.versionacceptedVersion
dc.rights.copyright© The Author(s) 2018
dc.rights.accesslevelopenAccessfi
dc.relation.grantnumber258882
dc.subject.ysoominaisuudet
dc.subject.ysotyö
dc.subject.ysoprofiilit (tieto)
dc.subject.ysotyöntekijät
dc.subject.ysoanalyysi
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p8691
jyx.subject.urihttp://www.yso.fi/onto/yso/p1810
jyx.subject.urihttp://www.yso.fi/onto/yso/p30204
jyx.subject.urihttp://www.yso.fi/onto/yso/p1075
jyx.subject.urihttp://www.yso.fi/onto/yso/p6851
dc.rights.urlhttp://rightsstatements.org/page/InC/1.0/?language=en
dc.relation.doi10.1177/1094428118760690
dc.relation.funderSuomen Akatemiafi
dc.relation.funderAcademy of Finlanden
jyx.fundingprogramAkatemiatutkijan tehtävä, SAfi
jyx.fundingprogramResearch post as Academy Research Fellow, AoFen
jyx.fundinginformationThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by grants from the Academy of Finland to Anne Mäkikangas (Grant 258882), Ulla Kinnunen (Grant 124268), and Saija Mauno (Grant 124360).


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