Pan Fan, Liu Qingqing
School of Education Science, Huizhou University, Huizhou, China.
Business School, Beijing Technology and Business University, Beijing, China.
Front Psychol. 2024 May 3;15:1366850. doi: 10.3389/fpsyg.2024.1366850. eCollection 2024.
This study informed researchers about the performance of different level-specific and target-specific model fit indices in the Multilevel Latent Growth Model (MLGM) with unbalanced design. As the use of MLGMs is relatively new in applied research domain, this study helped researchers using specific model fit indices to evaluate MLGMs. Our simulation design factors included three levels of number of groups (50, 100, and 200) and three levels of unbalanced group sizes (5/15, 10/20, and 25/75), based on simulated datasets derived from a correctly specified MLGM. We evaluated the descriptive information of the model fit indices under various simulation conditions. We also conducted ANOVA to calculated the extent to which these fit indices could be influenced by different design factors. Based on the results, we made recommendations for practical and theoretical research about the fit indices. CFI- and TFI-related fit indices performed well in the MLGM and could be trustworthy to use to evaluate model fit under similar conditions found in applied settings. However, RMSEA-related fit indices, SRMR-related fit indices, and chi square-related fit indices varied by the factors included in this study and should be used with caution for evaluating model fit in the MLGM.
本研究让研究人员了解了不平衡设计的多层潜在增长模型（MLGM）中不同水平特定和目标特定模型拟合指数的表现。由于MLGM在应用研究领域的使用相对较新，本研究帮助使用特定模型拟合指数的研究人员评估MLGM。我们的模拟设计因素包括基于从正确设定的MLGM导出的模拟数据集的三个组数量水平（50、100和200）以及三个不平衡组大小水平（5/15、10/20和25/75）。我们评估了各种模拟条件下模型拟合指数的描述性信息。我们还进行了方差分析以计算这些拟合指数受不同设计因素影响的程度。基于结果，我们对拟合指数的实践和理论研究提出了建议。与CFI和TFI相关的拟合指数在MLGM中表现良好，在应用环境中发现的类似条件下可用于评估模型拟合且值得信赖。然而，与RMSEA相关的拟合指数、与SRMR相关的拟合指数以及与卡方相关的拟合指数因本研究中包含的因素而异，在评估MLGM中的模型拟合时应谨慎使用。
