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：我们提出一种含协变量的多层潜在类别分析两步估计法。第一步估计观测项目的测量模型，第二步将协变量加入模型，同时保持测量模型参数固定。我们讨论了模型可识别性，并推导出期望最大化算法以实现估计器的高效运算。通过大规模模拟研究证明：（i）该方法与现有多层潜在类别分析的逐步估计器性能相似，但计算时间显著减少；（ii）与一步估计器相比，该方法能产生近似无偏的参数估计，且效率损失可忽略不计。该提案通过一项公民准则预测因素的跨国分析进行实证说明。