Growth Mixture Models Outperform Simpler Clustering Algorithms When Detecting Longitudinal Heterogeneity, Even With Small Sample Sizes
Daniel Patrick Martin, Timo von Oertzen · Structural Equation Modeling A Multidisciplinary Journal · 2014
Identifying subpopulations based on longitudinal trajectories can provide new avenues to answer theoretically interesting research questions. Although many techniques to accomplish this task exist, a common method used in psychology is the growth mixture model. Recent simulations have found that this analytic method shows a decline in performance for smaller sample sizes commonly found in psychological research (Kim, 2012; Peugh & Fan, 2012). This raises this question: Are there better methods available for smaller sample sizes? Monte Carlo simulations were used to explicitly compare growth mixture models with other clustering methods, ranging on a spectrum from not informed to very informed, across different simulation conditions. To compare results both between and within analytic method, Kullback–Leibler divergence is introduced as a measure of cluster solution misfit. Results show that despite this decreased performance for smaller sample sizes, growth mixture models still outperform simpler, more general clustering methods.