Growth mixture modeling (GMM) and its variants, which group individuals based on similar longitudinal growth trajectories, are quite popular in developmental and clinical science. However, research addressing the validity of GMM-identified latent subgroupings is limited. This Monte Carlo simulation tests the efficiency of GMM in identifying known subgroups (k = 1–4) across various combinations of distributional characteristics, including skew, kurtosis, sample size, intercept effect size, patterns of growth (none, linear, quadratic, exponential), and proportions of observations within each group. In total, 1,955 combinations of distributional parameters were examined, each with 1,000 replications (1,955,000 simulations). Using standard fit indices, GMM often identified the wrong number of groups. When one group was simulated with varying skew and kurtosis, GMM often identified multiple groups. When two groups were simulated, GMM performed well only when one group had steep growth (whether linear, quadratic, or exponential). When three to four groups were simulated, GMM was effective primarily when intercept effect sizes and sample sizes were large, an uncommon state of affairs in real-world applications. When conditions were less ideal, GMM often underestimated the correct number of groups when the true number was between two and four. Results suggest caution in interpreting GMM results, which sometimes get reified in the literature.

生长混合模型 (GMM) 及其变体基于相似的纵向生长轨迹对个体进行分组，在发育和临床科学中非常流行。然而，解决 GMM 识别的潜在子分组有效性的研究是有限的。这个蒙特卡罗模拟测试了 GMM 在识别已知子组（k= 1-4) 分布特征的各种组合，包括偏斜、峰度、样本大小、截距效应大小、增长模式（无、线性、二次、指数）以及每组内的观察比例。总共检查了 1,955 个分布参数组合，每个组合有 1,000 次重复（1,955,000 次模拟）。使用标准拟合指数，GMM 经常识别错误的组数。当用不同的偏斜和峰度模拟一组时，GMM 通常会识别出多个组。当模拟两组时，GMM 仅在一组有急剧增长（无论是线性、二次还是指数）时表现良好。当模拟三到四组时，GMM 主要在截距效应量和样本量大时有效，现实世界应用程序中不常见的情况。当条件不太理想时，当真实数字在 2 到 4 之间时，GMM 通常会低估正确的组数。结果表明在解释 GMM 结果时要谨慎，这些结果有时会在文献中得到具体化。