We propose a two-step estimator for multilevel latent class analysis (LCA) with covariates. The measurement model for observed items is estimated in its first step, and in the second step covariates are added in the model, keeping the measurement model parameters fixed. We discuss model identification, and derive an Expectation Maximization algorithm for efficient implementation of the estimator. By means of an extensive simulation study we show that (i) this approach performs similarly to existing stepwise estimators for multilevel LCA but with much reduced computing time, and (ii) it yields approximately unbiased parameter estimates with a negligible loss of efficiency compared to the one-step estimator. The proposal is illustrated with a cross-national analysis of predictors of citizenship norms.
翻译:我们为多级潜值级分析(LCA)提出了一个具有共变量的两步估计值。观测项目的测量模型在第一步是估算的,在第二步是估算的,在模型中添加了共变量,以保持测量模型参数的固定。我们讨论模型识别,并得出一个期望最大化算法,以高效地执行估计值。通过广泛的模拟研究,我们发现:(一)这一方法与现有多级潜值级分析的分步估计值相似,但计算时间大大缩短,以及(二)它得出了近乎公正的参数估计值,与一步测算器相比,效率损失微乎其微。提案用对公民规范预测器的跨国分析来说明。</s>