Multilevel Latent Profile Analysis With Covariates : Identifying Job Characteristics Profiles in Hierarchical Data as an Example
Mäkikangas, 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. Organizational Research Methods, 21(4), 931-954. https://doi.org/10.1177/1094428118760690
Published inOrganizational Research Methods
© The Author(s) 2018
Latent 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. ...
ISSN Search the Publication Forum1094-4281
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Related funder(s)Academy of Finland
Funding program(s)Academy Research Fellow, AoF
Additional information about fundingThe 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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