A1 Journal article (refereed)
Multilevel Latent Profile Analysis With Covariates : Identifying Job Characteristics Profiles in Hierarchical Data as an Example (2018)


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


JYU authors or editors


Publication details

All authors or editors: Mäkikangas, Anne; Tolvanen, Asko; Aunola, Kaisa; Feldt, Taru; Mauno, Saija; Kinnunen, Ulla

Journal or series: Organizational Research Methods

ISSN: 1094-4281

eISSN: 1552-7425

Publication year: 2018

Volume: 21

Issue number: 4

Pages range: 931-954

Publisher: Sage Publications

Publication country: United States

Publication language: English

DOI: https://doi.org/10.1177/1094428118760690

Publication open access: Not open

Publication channel open access:

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/60696


Abstract

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.


Keywords: profiles (information); analysis; properties; work; employees

Free keywords: multilevel latent profile analysis; clustered data; hierarchical structure; job demand-control-support model


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Ministry reporting: Yes

Reporting Year: 2018

JUFO rating: 2


Last updated on 2021-20-09 at 15:36