Department of Psychology, University of Kansas.
Department of Psychology, Indiana University-Purdue University Indianapolis.
Multivariate Behav Res. 2020 Jan-Feb;55(1):87-101. doi: 10.1080/00273171.2019.1608799. Epub 2019 May 17.
Ordinal missing data are common in measurement equivalence/invariance (ME/I) testing studies. However, there is a lack of guidance on the appropriate method to deal with ordinal missing data in ME/I testing. Five methods may be used to deal with ordinal missing data in ME/I testing, including the continuous full information maximum likelihood estimation method (FIML), continuous robust FIML (rFIML), FIML with probit links (pFIML), FIML with logit links (lFIML), and mean and variance adjusted weight least squared estimation method combined with pairwise deletion (WLSMV_PD). The current study evaluates the relative performance of these methods in producing valid chi-square difference tests ([Formula: see text]) and accurate parameter estimates. The result suggests that all methods except for WLSMV_PD can reasonably control the type I error rates of [Formula: see text] tests and maintain sufficient power to detect noninvariance in most conditions. Only pFIML and lFIML yield accurate factor loading estimates and standard errors across all the conditions. Recommendations are provided to researchers based on the results.
等级缺失数据在测量等效性/不变性（ME/I）测试研究中很常见。然而，在 ME/I 测试中处理等级缺失数据的适当方法方面缺乏指导。在 ME/I 测试中处理等级缺失数据时，可以使用五种方法，包括连续全信息最大似然估计法（FIML）、连续稳健 FIML（rFIML）、带有概率单位链接的 FIML（pFIML）、带有逻辑链接的 FIML（lFIML）以及均值和方差调整权重最小二乘估计法与成对删除（WLSMV_PD）相结合。本研究评估了这些方法在产生有效卡方差异检验（[公式：见正文]）和准确参数估计方面的相对性能。结果表明，除 WLSMV_PD 外，所有方法都可以合理地控制[公式：见正文]检验的Ⅰ类错误率，并在大多数情况下保持足够的能力来检测不变性。只有 pFIML 和 lFIML 在所有条件下都能产生准确的因子负荷估计值和标准误差。根据结果为研究人员提供了建议。
