Abstract

The latent growth curve modeling (LGM) and random effects modeling (REM) frameworks are analytically and empirically equivalent for intrinsically linear models and used interchangeably for intrinsically nonlinear models. However, while LGM provides overall model fit indices, REM does not. Overall model fit indices are useful because they evaluate how well a specified model fits data. This paper proposes to translate model fit concepts from LGM to REM to help researchers compute overall model fit indices, including the model chi-square (χ2${\chi }^2$${\chi }^2$), comparative fit index (CFI), root mean squared error of approximation (RMSEA), and standardized root mean squared residual (SRMR). Three empirical examples were used as illustrations.

摘要

潜在增长曲线模型 (LGM) 和随机效应模型 (REM) 框架在分析和经验上对于本质线性模型是等效的，并且对于本质非线性模型可互换使用。然而，虽然 LGM 提供了总体模型拟合指数，但 REM 却没有。总体模型拟合指数非常有用，因为它们评估指定模型对数据的拟合程度。本文建议将模型拟合概念从 LGM 转换为 REM，以帮助研究人员计算总体模型拟合指数，包括模型卡方（χ2${\chi }^2$${\chi }^2$)、比较拟合指数 (CFI)、近似均方根误差 (RMSEA) 和标准化残差均方根 (SRMR)。使用三个经验例子作为说明。