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Growth mixture modeling (GMM) combines latent growth curve and mixture modeling approaches and is typically used to identify discrete trajectories following major life stressors (MLS). However, GMM is often applied to data that does not meet the statistical assumptions of the model (e.g., within-class normality) and researchers often do not test additional model constraints (e.g., homogeneity of variance across classes), which can lead to incorrect conclusions regarding the number and nature of the trajectories. We evaluate how these methodological assumptions influence trajectory size and identification in the study of resilience to MLS.

the journals of gerontology. series b, psychological sciences and social sciences

The Use of Growth Mixture Modeling for Studying Resilience to Major Life Stressors in Adulthood and Old Age: Lessons for Class Size and Identification and Model Selection