Chen Po-Yi, Wu Wei, Brandt Holger, Jia Fan
Department of Psychological Science, University of Texas Rio Grande Valley, Edinburg, TX, USA.
Department of Psychology, Indiana University-Purdue University Indianapolis, Indianapolis, IN, USA.
Behav Res Methods. 2020 Dec;52(6):2567-2587. doi: 10.3758/s13428-020-01415-2.
In measurement invariance testing, when a certain level of full invariance is not achieved, the sequential backward specification search method with the largest modification index (SBSS_LMFI) is often used to identify the source of non-invariance. SBSS_LMFI has been studied under complete data but not missing data. Focusing on Likert-type scale variables, this study examined two methods for dealing with missing data in SBSS_LMFI using Monte Carlo simulation: robust full information maximum likelihood estimator (rFIML) and mean and variance adjusted weighted least squared estimator coupled with pairwise deletion (WLSMV_PD). The result suggests that WLSMV_PD could result in not only over-rejections of invariance models but also reductions of power to identify non-invariant items. In contrast, rFIML provided good control of type I error rates, although it required a larger sample size to yield sufficient power to identify non-invariant items. Recommendations based on the result were provided.
在测量不变性检验中，当未达到一定水平的完全不变性时，通常使用具有最大修正指数的顺序向后规范搜索方法（SBSS_LMFI）来识别非不变性的来源。SBSS_LMFI已在完整数据而非缺失数据的情况下进行了研究。本研究聚焦于李克特量表类型变量，使用蒙特卡罗模拟检验了SBSS_LMFI中处理缺失数据的两种方法：稳健全信息最大似然估计器（rFIML）以及均值和方差调整加权最小二乘估计器与成对删除法相结合（WLSMV_PD）。结果表明，WLSMV_PD不仅可能导致对不变性模型的过度拒绝，还会降低识别非不变项目的功效。相比之下，rFIML对I型错误率有良好的控制，尽管它需要更大的样本量才能产生足够的功效来识别非不变项目。基于该结果给出了相关建议。
