摘要
本研究通过蒙特卡洛模拟考查了采用全息极大似然估计进行缺失数据建模时辅助变量的作用。具体考查了辅助变量与研究变量的共缺机制、共缺率、相关程度、辅助变量数目与样本量等因素对参数估计结果精确性的影响。结果表明,当辅助与研究变量共缺时:(1)对于完全随机缺失的辅助变量,结果更容易出现偏差;(2)对于MAR-MAR组合机制,纳入单个辅助变量是有益的;对于MAR-MCAR或MAR-MNAR组合机制,纳入多于一个辅助变量的效果更好;(3)纳入与研究变量低相关的辅助变量对结果也是有益的。
In social and behavioral studies, missing data cannot be avoided in the process of data collection, especially in longitudinal studies. Because sample with missing data lose the balance characteristics of their complete counterparts, which may distort parameter estimates and degrade the performance of confidence intervals, special methods have to be developed for these analysis. Two modern missing data analysis techniques, maximum likelihood estimation and multiple imputation, have been widely studied in the methodological literature during the last decade. Since the maximum likelihood estimation and multiple imputation require the MAR（missing at random） assumption, including auxiliary variables can help fine-tune the missing data handling procedure, either by reducing bias or by increasing power. A useful auxiliary variable is a potential cause or a correlate of the incomplete variables in the analysis model. Notably, Graham（2003） proposed a ＂saturated correlates model＂, which allows us to include auxiliary variables in FIML-based structural equation models easily. However, some questions about the inclusion of auxiliary variables are needed to further study. The main research question was under what condition the auxiliary variables will be effective in the FIML-based structural equation modeling. The current study investigates the effect of including auxiliary variables during estimation of structural equation modeling parameters with FIML estimation through Monte Carlo simulation. It focused on the missing values of the auxiliary variables and variables of interests simultaneously. The simulation repeated 5,000 times for each of 576 combinations： common missing rates（5 percent, 10 percent, 15 percent, and 20 percent）, missingmechanism combinations（MCAR-MCAR, MCAR-MAR, MCAR-MNAR, MAR-MCAR, MAR-MAR, and MAR-MNAR）, correlations（low, moderate to high）, number of auxiliary variables（1, 3, 5）, and sample sizes（100, 200, 500, 1000）. The evaluation criteria are bias and confidence in
作者
王孟成
邓俏文
WANG Meng-Cheng DENG Qiaowen(Department of Psychology, Guangzhou Universit Center for Psychometric and Latent Variable Modeling, Guangzhou University, Guangzhou 510006, Chin)
出处
《心理学报》
CSSCI
CSCD
北大核心
2016年第11期1489-1498,共10页
Acta Psychologica Sinica
基金
国家自然科学基金(31400904)
广州大学"创新强校工程"(2014WQNCX069)项目资助
关键词
缺失数据
缺失机制
结构方程
全息极大似然估计
辅助变量
蒙特卡洛模拟
missing data
missing mechanism
SEM
full information maximum likelihood
auxiliary variable
Monte Carlo simulation
