Missing data and ordinal indicators are common in applied research involving latent constructs. Unfortunately, ordinal indicators violate the linearity assumption for conventional CFA that is routinely used to provide structural validity evidence for measurement instruments. Although robust maximum likelihood estimator (MLR) can deal with both missing data and nonnormality, it is generally inappropriate for ordinal indicators. Categorical estimation methods such as weighted least square mean and variance adjusted (WLSMV) method, or MLR or maximum likelihood (ML) that justly treats ordinal indicators as categorical (MLR-CAT or ML-CAT, respectively) have been recommended for ordinal dependent variables. However, performances of these categorical estimators in the presence of missing data have not been empirically examined. The current study systematically investigates the relative performances of WLSMV, MLR, MLR-CAT, and ML-CAT under different conditions of missing data amount and mechanism, sample size, level of indicator distribution, and number of indicator categories. Results generally favor MLR-CAT so long as the sample size is not too small (>200) to result in convergence problems.

中文翻译：

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用于具有缺失数据的序变量的验证性因子分析的估计器的性能

在涉及潜在结构的应用研究中，缺失数据和有序指标很常见。不幸的是，序数指标违反了常规 CFA 的线性假设，该假设通常用于为测量工具提供结构有效性证据。虽然鲁棒最大似然估计器 (MLR) 可以处理缺失数据和非正态性，但它通常不适用于序数指标。分类估计方法，如加权最小二乘均值和方差调整 (WLSMV) 方法，或 MLR 或最大似然 (ML) 将有序指标正确地视为分类（分别为 MLR-CAT 或 ML-CAT）已被推荐用于有序因变量. 然而，这些分类估计器在存在缺失数据的情况下的表现尚未经过实证检验。本研究系统地考察了 WLSMV、MLR、MLR-CAT 和 ML-CAT 在缺失数据量和机制、样本量、指标分布水平、指标类别数量等不同条件下的相对表现。结果通常有利于 MLR-CAT，只要样本量不会太小 (>200) 导致收敛问题。