Identifying subgroups: Part 1: Patterns among cross-sectional data
Author(s) -
Lee Christopher S,
Faulkner Kenneth M,
Thompson Jessica H
Publication year - 2020
Publication title -
european journal of cardiovascular nursing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.914
H-Index - 50
eISSN - 1873-1953
pISSN - 1474-5151
DOI - 10.1177/1474515120911323
Subject(s) - latent class model , medicine , class (philosophy) , cross sectional study , econometrics , data science , statistics , artificial intelligence , computer science , mathematics , pathology
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.
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