中文摘要：

对含有非随机缺失数据的潜变量增长模型，为了考察基于不同假设的缺失数据处理方法：极大似然（ML）方法与Diggle—Kenward选择模型的优劣，通过MonteCarlo模拟研究，比较两种方法对模型中增长参数估计精度及其标准误估计的差异，并考虑样本量、非随机缺失比例和随机缺失比例的影响。结果表明，符合前提假设的Diggle-Kenward选择模型的参数估计精度普遍高于ML方法；对于标准误估计值，ML方法存在一定程度的低估，得到的置信区间覆盖比率也明显低于Diggle—Kenward选择模型。

英文摘要：

Longitudinal data analysis is a widely-used technique in psychological studies. However, since it is time consuming with a large number of repeated observations, missing data is a common problem and usually occurs via the missing not at random （MNAR） mechanism. Methods for handling missing data have developed for a long time; however, because the MNAR mechanism itself cannot be tested, nor can the assumptions of different models under MNAR, it is still not an easy task for practitioners to select an appropriate method for handling the MNAR missing data. Inappropriate methods may bias parameter estimates and even mislead the study results due to the violation of model assumptions. The objective of the current study is to investigate the effects of method selection when fitting a latent growth curve model with longitudinal datasets. Two approaches under different assumptions were compared in handling the MNAR missingness in a five-wave longitudinal dataset using the Monte Carlo simulation： one is the Diggle-Kenward selection model under the MNAR mechanism, the other is the Maximum Likelihood （ML） method under the MAR mechanism. Three factors were simultaneously considered in this study： （i） the sample size （100, 300, 500, 1000）, （ii） the percentage of MNAR missing data （5%, 10%, 20% 40%）, and （iii） the percentage of MAR missing data （0%, 10%, 20%）. So a total of 4×4×3=48 conditions were generated, and 500 replicates were used in each of the conditions. The performances in estimating parameters （the means and variances of intercept and slope, i.e.μi, μs, σi2 and σs2） of these two approaches were then evaluated according to two criteria, namely, root mean square error （RMSE）, and coverage rates of the 95% CIs. The estimations of standard errors （SEs） were also considered. Results indicated that： （i） higher precision of estimates were obtained from the Diggle-Kenward selection model, especially under the conditions of high percentage of MNAR missing data. ?