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.

本研究探讨了在多水平多指标多原因模型中省略协变量交互效应对参数估计的影响，以及当水平间、水平内或跨水平交互作用被忽略时拟合指数对模型错误指定的敏感性。被遗漏在模型中。在不同的聚类数量、聚类大小、类内相关性以及总体模型中交互效应大小的条件下，对正确模型和错误指定模型中产生的参数估计进行比较。结果显示，两个主效应被高估了大约交互效应大小的一半，并且水平间因子均值被低估。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均对交互效应的遗漏不敏感。信息标准的敏感性主要取决于被忽略的交互作用的大小以及交互作用的位置（即，在级别之间、级别内或跨级别）。讨论了基于调查结果的影响和建议。