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. 2009;33(6):565-576.
doi: 10.1177/0165025409343765.

Growth Mixture Modeling: A Method for Identifying Differences in Longitudinal Change Among Unobserved Groups

Nilam Ram  1 , Kevin J Grimm
Affiliations

Growth Mixture Modeling: A Method for Identifying Differences in Longitudinal Change Among Unobserved Groups

Nilam Ram et al. Int J Behav Dev. 2009.

Abstract

Growth mixture modeling (GMM) is a method for identifying multiple unobserved sub-populations, describing longitudinal change within each unobserved sub-population, and examining differences in change among unobserved sub-populations. We provide a practical primer that may be useful for researchers beginning to incorporate GMM analysis into their research. We briefly review basic elements of the standard latent basis growth curve model, introduce GMM as an extension of multiple-group growth modeling, and describe a four-step approach to conducting a GMM analysis. Example data from a cortisol stress-response paradigm are used to illustrate the suggested procedures.

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Figures

Figure 1
Figure 1
Step-by-step procedures for implementing GMM analysis. Further explanation provided in the text.
Figure 2
Figure 2
Cortisol levels obtained from 34 individuals across 9 occasions.
Figure 3
Figure 3
Mean and individual trajectories obtained from the 2-ClassMeans+Covs+Pattern model. Panel A = “typical” individuals (n = 17); Panel B = “chronic-stress” individuals (n = 17).
See this image and copyright information in PMC

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