Identifying subgroups: Part 1: Patterns among cross-sectional data
Journal article
Christopher.Lee, Kenneth M. Faulkner and Jessica H. Thompson. (2020). Identifying subgroups: Part 1: Patterns among cross-sectional data. European Journal of Cardiovascular Nursing. 19(4), pp. 359-365. https://doi.org/10.1177/1474515120911323
Authors | Christopher.Lee, Kenneth M. Faulkner and Jessica H. Thompson |
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Abstract | Non-experimental designs are common in nursing and allied health research wherein study participants often represent more than a single population or interest. Hence, methods used to identify subgroups and explore heterogeneity have become popular. Latent class mixture modeling is a versatile and person-centered analytic strategy that allows us to study questions about subgroups within samples. In this article, a worked example of latent class mixture modeling is presented to help expose researchers to the nuances of this analytic strategy. |
Keywords | latent class mixture modeling; latent models; structural equation modeling; subgroup analysis |
Year | 2020 |
Journal | European Journal of Cardiovascular Nursing |
Journal citation | 19 (4), pp. 359-365 |
Publisher | Oxford University Press |
ISSN | 1474-5151 |
Digital Object Identifier (DOI) | https://doi.org/10.1177/1474515120911323 |
Scopus EID | 2-s2.0-85081563812 |
Publisher's version | File Access Level Controlled |
Publication process dates | |
Deposited | 28 Apr 2021 |
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https://acuresearchbank.acu.edu.au/item/8vy7z/identifying-subgroups-part-1-patterns-among-cross-sectional-data
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