Department of Educational Psychology and Counseling, National Taiwan Normal University, Taipei, Taiwan, 106308.
Department of Psychological Sciences, University of California Merced, Merced, CA, USA.
Behav Res Methods. 2024 Oct;56(7):6498-6519. doi: 10.3758/s13428-024-02367-7. Epub 2024 Feb 28.
Multi-informant studies are popular in social and behavioral science. However, their data analyses are challenging because data from different informants carry both shared and unique information and are often incomplete. Using Monte Carlo Simulation, the current study compares three approaches that can be used to analyze incomplete multi-informant data when there is a distinction between reference and nonreference informants. These approaches include a two-method measurement model for planned missing data (2MM-PMD), treating nonreference informants' reports as auxiliary variables with the full-information maximum likelihood method or multiple imputation, and listwise deletion. The result suggests that 2MM-PMD, when correctly specified and data are missing at random, has the best overall performance among the examined approaches regarding point estimates, type I error rates, and statistical power. In addition, it is also more robust to data that are not missing at random.
多 informant 研究在社会和行为科学中很受欢迎。然而，它们的数据分析具有挑战性，因为来自不同 informant 的数据既具有共同信息又具有独特信息，并且通常是不完整的。本研究使用蒙特卡罗模拟比较了三种方法，这些方法可用于在参考和非参考 informant 之间存在区别时分析不完整的多 informant 数据。这些方法包括用于计划缺失数据的两方法测量模型（2MM-PMD），将非参考 informant 的报告视为具有完全信息最大似然法或多重插补的辅助变量，以及逐项删除。结果表明，在正确指定且数据随机缺失的情况下，2MM-PMD 在检查的方法中在点估计，I 型错误率和统计功效方面具有最佳的整体性能。此外，它对非随机缺失的数据也更具鲁棒性。
