Following the procedure outlined in Jung and Wickrama,36 we first specified a single-class growth curve model, which gave the overall growth curve of the sample. This overall growth curve, included in Figure 1, closely matches the sample means for all three cohorts. Next, we derived multiple trajectories using latent class growth analysis (LCGA) and growth mixture modeling (GMM). In LCGA, individuals within each trajectory group are constrained to have the same intercept and slope, but in GMM, individuals in each trajectory group can have different intercepts and/or slopes. When determining the optimal number of trajectory classes, the usual approach is to start with LCGA, which is less computationally burdensome because within-class variances are fixed to zero, then move to GMM, freeing intercept and/or slope variances for the trajectory classes and then allowing each class to have its own unique variance. Selection of the optimum model can be determined using Lo-Mendell-Rubin Likelihood Ratio Test (LMR-LRT) p-values and the Bootstrapped Likelihood Ratio Test (BLRT).
