ABSTRACT Measurement invariance (MI) overtime is required for meaningful interpretation of changes in the latent change score model (LCSM). In this simulation study, we investigate the impact of measurement noninvariance on the estimation of LCSM when proportional change model, dual change model, and their bivariate versions are used with mean composites or MI-assumed measurement models. The results show that even noninvariance in one item could result in severe bias in the LCSM parameter estimates and false statistical inferences (e.g., Type I error). However, the impact depends on the simulation factors, fitted models, and estimated parameters. For example, the intercept-factor mean is biased in proportional change model but not in dual change model. In bivariate LCSM the parameter estimates of only the noninvariant variable are biased except coupling effects. Model fit indices such as RMSEA and SRMR are insensitive to the ignored noninvariance. Discussions and implications of findings are presented.

中文翻译：

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测量非不变性对潜在变化评分建模的影响：蒙特卡罗模拟研究

摘要 测量不变性 (MI) 超时需要对潜在变化评分模型 (LCSM) 的变化进行有意义的解释。在本模拟研究中，我们研究了当比例变化模型、双变化模型及其双变量版本与平均复合或 MI 假设测量模型一起使用时，测量非不变性对 LCSM 估计的影响。结果表明，即使一个项目的非不变性也可能导致 LCSM 参数估计的严重偏差和错误的统计推断（例如，I 类错误）。但是，影响取决于模拟因素、拟合模型和估计参数。例如，截距因子均值在比例变化模型中有偏差，但在双变化模型中没有偏差。在双变量 LCSM 中，除了耦合效应之外，只有非不变变量的参数估计是有偏差的。RMSEA 和 SRMR 等模型拟合指数对忽略的非不变性不敏感。介绍了研究结果的讨论和影响。