Abstract

The current study aims to investigate mediation in regression mixture models. There has been little research that has examined the combination of mediation and regression mixture models to determine if there are latent subgroups that vary in their levels of mediation. This investigation aims to address this gap by simulating varying conditions of sample size, number of latent classes, mixing proportions, class intercept separation, direct effects, and class separation on mediating effects. Information criteria (i.e., AIC, BIC, aBIC) and likelihood ratio tests (i.e., LMR, VLMR, and BLRT) were evaluated for model selection. The results suggest that the BIC and BLRT perform best at identifying the correct number of latent classes. The class enumeration indices improved in accuracy as sample size, class intercept separation, and separation on the mediating effect increased. The current investigation identifies conditions where class enumeration is most accurate with mediation in regression mixture models.

摘要

当前的研究旨在调查回归混合模型中的调解。很少有研究检查调解和回归混合模型的组合，以确定是否存在调解水平不同的潜在子组。本调查旨在通过模拟样本大小、潜在类数量、混合比例、类截距分离、直接效应和类分离对中介效应的不同条件来解决这一差距。评估信息标准（即 AIC、BIC、aBIC）和似然比检验（即 LMR、VLMR 和 BLRT）以选择模型。结果表明 BIC 和 BLRT 在识别正确数量的潜在类别方面表现最佳。类枚举指标在样本量、类截距分离、和分离对中介作用增强。当前的调查确定了在回归混合模型中类枚举最准确的条件。