This article compares two missing data procedures, full information maximum likelihood (FIML) and multiple imputation (MI), to investigate their relative performances in relation to the results from analyses of the original complete data or the hypothetical data available before missingness occurred. By expressing the FIML estimator as a special MI estimator, we predicted the expected patterns of discrepancy between the two estimators. Via Monte Carlo simulation studies where we have access to the original complete data, we compare the performance of FIML and MI estimators to that of the complete data maximum likelihood (ML) estimator under a wide range of conditions, including differences in sample size, percent of missingness, and degrees of model misfit. Our study confirmed well-known knowledge that the two estimators tend to yield essentially equivalent results to each other and to those from analysis of complete data when the postulated model is correctly specified. However, some noteworthy patterns of discrepancies were found between the FIML and MI estimators when the hypothesized model does not hold exactly in the population: MI-based parameter estimates, comparative fit index (CFI), and the Tucker Lewis index (TLI) tend to be closer to the counterparts of the complete data ML estimates, whereas FIML-based chi-squares and root mean square error of approximation (RMSEA) tend to be closer to the counterparts of the complete data ML estimates. We explained the observed patterns of discrepancy between the two estimators as a function of the interplay between the parsimony and accuracy of the imputation model. We concluded by discussing practical and methodological implications and issues for further research. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

中文翻译：

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结构方程建模中的全信息最大似然和多重插补与缺失数据的比较。

本文比较了两种缺失数据程序，全信息最大似然 (FIML) 和多重插补 (MI)，以研究它们与原始完整数据的分析结果或缺失发生之前可用的假设数据相关的相对性能。通过将 FIML 估计量表示为特殊 MI 估计量，我们预测了两个估计量之间的预期差异模式。通过我们可以访问原始完整数据的蒙特卡罗模拟研究，我们将 FIML 和 MI 估计器的性能与完整数据最大似然 (ML) 估计器在各种条件下的性能进行了比较，包括样本大小、百分比的差异缺失率和模型失配程度。我们的研究证实了众所周知的知识，即当正确指定假设模型时，两个估计量往往会产生彼此基本相同的结果，并且与来自完整数据分析的结果相同。然而，当假设模型不完全适用于总体时，在 FIML 和 MI 估计量之间发现了一些值得注意的差异模式：基于 MI 的参数估计、比较拟合指数 (CFI) 和 Tucker Lewis 指数 (TLI) 倾向于更接近完整数据 ML 估计的对应物，而基于 FIML 的卡方和近似均方根误差 (RMSEA) 往往更接近完整数据 ML 估计的对应物。我们将观察到的两个估计量之间的差异模式解释为插补模型的简约性和准确性之间相互作用的函数。我们最后讨论了实际和方法论的影响以及进一步研究的问题。（PsycInfo 数据库记录 (c) 2021 APA，保留所有权利）。