Shi Dexin, DiStefano Christine, Zheng Xiaying, Liu Ren, Jiang Zhehan
University of South Carolina, Columbia, SC, USA.
American Institutes for Research, Washington, DC, USA.
Int J Behav Dev. 2021 Mar;45(2):179-192. doi: 10.1177/0165025420979365. Epub 2021 Jan 7.
This study investigates the performance of robust ML estimators when fitting and evaluating small sample latent growth models (LGM) with non-normal missing data. Results showed that the robust ML methods could be used to account for non-normality even when the sample size is very small (e.g., < 100). Among the robust ML estimators, "MLR" was the optimal choice, as it was found to be robust to both non-normality and missing data while also yielding more accurate standard error estimates and growth parameter coverage. However, the choice "MLMV" produced the most accurate values for the Chi-square test statistic under conditions studied. Regarding the goodness of fit indices, as sample size decreased, all three fit indices studied (i.e., CFI, RMSEA, and SRMR) exhibited worse fit. When the sample size was very small (e.g., < 60), the fit indices would imply that a proposed model fit poorly, when this might not be actually the case in the population.
本研究调查了在拟合和评估具有非正态缺失数据的小样本潜在增长模型（LGM）时稳健最大似然估计量的性能。结果表明，即使样本量非常小（例如，<100），稳健最大似然方法也可用于处理非正态性。在稳健最大似然估计量中，“MLR”是最佳选择，因为它对非正态性和缺失数据均具有稳健性，同时还能产生更准确的标准误差估计值和增长参数覆盖率。然而，在所研究的条件下，“MLMV”选择产生了卡方检验统计量的最准确值。关于拟合优度指数，随着样本量的减少，所研究的所有三个拟合指数（即CFI、RMSEA和SRMR）均显示拟合效果变差。当样本量非常小（例如，<60）时，拟合指数可能会表明所提出的模型拟合不佳，但在总体中实际情况可能并非如此。
