Literature DB >> 32126820

Identifying subgroups: Part 1: Patterns among cross-sectional data.

Christopher S Lee1, Kenneth M Faulkner1, Jessica H Thompson1.   

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.

Entities:  

Keywords:  Latent class mixture modeling; latent models; structural equation modeling; subgroup analysis

Mesh:

Year:  2020        PMID: 32126820     DOI: 10.1177/1474515120911323

Source DB:  PubMed          Journal:  Eur J Cardiovasc Nurs        ISSN: 1474-5151            Impact factor:   3.908


  3 in total

1.  Identifying unique profiles of perceived dyspnea burden in heart failure.

Authors:  Kenneth M Faulkner; Corrine Y Jurgens; Quin E Denfeld; Karen S Lyons; Jessica Harman Thompson; Christopher S Lee
Journal:  Heart Lung       Date:  2020-05-18       Impact factor: 2.210

2.  Validating online approaches for rare disease research using latent class mixture modeling.

Authors:  Andrew A Dwyer; Ziwei Zeng; Christopher S Lee
Journal:  Orphanet J Rare Dis       Date:  2021-05-10       Impact factor: 4.123

3.  The association between hospital nursing resource profiles and nurse and patient outcomes.

Authors:  Eileen T Lake; Kathryn A Riman; Christopher S Lee
Journal:  J Nurs Manag       Date:  2022-02-17       Impact factor: 3.325

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.