Abstract

Longitudinal research often involves relatively small samples and missing values. Under these conditions, Bayesian estimation can still result in accurate parameter estimates for latent growth models (LGMs). However, researchers were limited in their options for assessing model fit. Several new (approximate) model fit indices have been introduced into the Bayesian structural equation modeling framework. Through a simulation study, we examined the performance of these indices for model fit and selection with a latent growth model (LGM). Specifically, this study was designed to assess the impact of different prior specifications on the fit indices across several sample sizes and missing data conditions. Findings suggested that priors that diverge from the population values can interfere with model fit and selection assessment, making correctly specified models appear misspecified. In addition, the approximate fit indices may be more suited for model selection rather than model fit assessment. Implications for applied researchers are discussed.

摘要

纵向研究通常涉及相对较小的样本和缺失值。在这些条件下，贝叶斯估计仍然可以为潜在增长模型 (LGM) 提供准确的参数估计。然而，研究人员在评估模型拟合方面的选择有限。贝叶斯结构方程建模框架中引入了几个新的（近似）模型拟合指数。通过模拟研究，我们使用潜在增长模型 (LGM) 检查了这些指标在模型拟合和选择方面的表现。具体来说，本研究旨在评估不同先前规范对多个样本大小和缺失数据条件下的拟合指数的影响。研究结果表明，存在分歧的先验从总体值可能会干扰模型拟合和选择评估，使正确指定的模型出现错误指定。此外，近似拟合指数可能更适合模型选择而不是模型拟合评估。讨论了对应用研究人员的影响。