Selecting the number of different classes that will be assumed to exist in the population is an important step in latent class analysis (LCA). The bootstrap likelihood ratio test (BLRT) provides a data-driven way to evaluate the relative adequacy of a (K – 1)-class model compared to a K-class model. However, very little is known about how to predict the power or the required sample size for the BLRT in LCA. Based on extensive Monte Carlo simulations, we provide practical effect size measures and power curves that can be used to predict power for the BLRT in LCA given a proposed sample size and a set of hypothesized population parameters. Estimated power curves and tables provide guidance for researchers wishing to size a study to have sufficient power to detect hypothesized underlying latent classes.

中文翻译：

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潜在类别分析中 Bootstrap 似然比检验的效应量、统计功效和样本量要求

选择假定存在于总体中的不同类别的数量是潜在类别分析 (LCA) 中的一个重要步骤。自举似然比检验 (BLRT) 提供了一种数据驱动的方法来评估 (K – 1) 类模型与 K 类模型相比的相对充分性。然而，对于如何在 LCA 中预测 BLRT 的功效或所需的样本量，我们知之甚少。基于广泛的 Monte Carlo 模拟，我们提供了实用的效应量度量和功效曲线，可用于在给定建议的样本量和一组假设的总体参数的情况下预测 LCA 中 BLRT 的功效。估计功效曲线和表格为希望调整研究规模以具有足够功效来检测假设的潜在潜在类别的研究人员提供指导。