The impact of nonnormality on full information maximum-likelihood estimation for structural equation models with missing data
The impact of nonnormality on full information maximum-likelihood estimation for structural equation models with missing data
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DOI：
10.1037//1082-989x.6.4.352
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发表时间：
2001-12-01
影响因子：
7
通讯作者：
Enders, CK
中科院分区：
文献类型：
--
作者：
Enders, CK
蒙特卡罗模拟检验了具有非正态指标变量的结构方程模型的全信息最大似然估计(FIML)。考察了4个自变量(缺失数据算法、缺失率、样本量和分布形状)对4个结果度量(参数估计偏差、参数估计效率、标准误差覆盖率和模型拒绝率)的影响。在完全随机缺失和随机缺失两种模式下，FIML参数估计涉及的偏差较小，通常比特别缺失数据技术更有效。然而，与结构方程建模中的完全数据最大似然估计类似，标准误差是负偏的，模型拒绝率被夸大。模拟结果表明，最近开发的缺失数据校正(例如，重新缩放的统计和自举)可以缓解源于非正态数据的问题。
A Monte Carlo simulation examined full information maximum-likelihood estimation (FIML) in structural equation models with nonnormal indicator variables. The impacts of 4 independent variables were examined (missing data algorithm, missing data rate, sample size, and distribution shape) on 4 outcome measures (parameter estimate bias, parameter estimate efficiency, standard error coverage, and model rejection rates). Across missing completely at random and missing at random patterns, FIML parameter estimates involved less bias and were generally more efficient than those of ad hoc missing data techniques. However, similar to complete-data maximum-likelihood estimation in structural equation modeling, standard errors were negatively biased and model rejection rates were inflated. Simulation results suggest that recently developed correctives for missing data (e.g., rescaled statistics and the bootstrap) can mitigate problems that stem from nonnormal data.