ABSTRACT

Factor mixture modeling (FMM) is generally complex with both unobserved categorical and unobserved continuous variables. We explore the potential of item parceling to reduce the model complexity of FMM and improve convergence and class enumeration accordingly. To this end, we conduct Monte Carlo simulations with three types of data, continuous, polytomous, and binary under two levels of model complexity, constrained FMM under strict invariance and relaxed FMM under scalar or metric invariance. The results show that item parceling could be advantageous for FMM with binary items but not with continuous or polytomous items.

中文翻译：

---

项目打包对因素混合建模性能的混合影响

摘要

因子混合模型 (FMM) 通常很复杂，包含未观察到的分类变量和未观察到的连续变量。我们探索了项目打包的潜力，以降低 FMM 的模型复杂性并相应地提高收敛性和类枚举。为此，我们在两个模型复杂度、严格不变性下的约束 FMM 和标量或度量不变性下的松弛 FMM 下使用三种类型的数据进行蒙特卡罗模拟，连续的、多态的和二元的。结果表明，项目打包对于具有二元项目的 FMM 可能是有利的，但对于连续或多头项目则不适用。