Cao Chunhua, Kim Eun Sook, Chen Yi-Hsin, Ferron John
University of Arkansas, Fayetteville, AR, USA.
University of South Florida, Tampa, FL, USA.
Educ Psychol Meas. 2021 Oct;81(5):817-846. doi: 10.1177/0013164421992407. Epub 2021 Feb 12.
This study examined the impact of omitting covariates interaction effect on parameter estimates in multilevel multiple-indicator multiple-cause models as well as the sensitivity of fit indices to model misspecification when the between-level, within-level, or cross-level interaction effect was left out in the models. The parameter estimates produced in the correct and the misspecified models were compared under varying conditions of cluster number, cluster size, intraclass correlation, and the magnitude of the interaction effect in the population model. Results showed that the two main effects were overestimated by approximately half of the size of the interaction effect, and the between-level factor mean was underestimated. None of comparative fit index, Tucker-Lewis index, root mean square error of approximation, and standardized root mean square residual was sensitive to the omission of the interaction effect. The sensitivity of information criteria varied depending majorly on the magnitude of the omitted interaction, as well as the location of the interaction (i.e., at the between level, within level, or cross level). Implications and recommendations based on the findings were discussed.
本研究考察了在多水平多指标多原因模型中遗漏协变量交互效应时对参数估计的影响，以及当模型中遗漏层间、层内或跨层交互效应时拟合指数对模型误设的敏感性。在聚类数量、聚类大小、组内相关以及总体模型中交互效应大小的不同条件下，比较了正确模型和误设模型产生的参数估计。结果表明，两个主效应被高估了约交互效应大小的一半，且层间因子均值被低估。比较拟合指数、塔克-刘易斯指数、近似均方根误差和标准化均方根残差对交互效应的遗漏均不敏感。信息准则的敏感性主要取决于遗漏交互效应的大小以及交互效应的位置（即层间、层内或跨层）。基于研究结果进行了讨论并给出了建议。
