What is it about?
Default approaches to growth mixture modelling may lead to incorrect determination of mixtures, thereby providing potentially misleading results. Group-based modelling strategies might inappropriately force incorrect mixtures to be determined. Thought needs to be given to how one constructs a mixture model - best driven by an understanding of data generating processes.
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Why is it important?
If insufficient care is taken when using growth mixture models, erroneous findings can emerge. Many published models may therefore be suspect. The issue highlighted here is poorly understood and not widely known about.
Perspectives
A key issue in modelling complexity is understanding data generation processes.
Professor Mark S Gilthorpe
University of Leeds
Read the Original
This page is a summary of: Challenges in modelling the random structure correctly in growth mixture models and the impact this has on model mixtures, Journal of Developmental Origins of Health and Disease, March 2014, Cambridge University Press,
DOI: 10.1017/s2040174414000130.
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