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首页> 外文期刊>Multivariate behavioral research >On the Mathematical Relationship Between Latent Change Score and Autoregressive Cross-Lagged Factor Approaches: Cautions for Inferring Causal Relationship Between Variables
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On the Mathematical Relationship Between Latent Change Score and Autoregressive Cross-Lagged Factor Approaches: Cautions for Inferring Causal Relationship Between Variables

机译：潜在变化得分与自回归交叉滞后因子方法之间的数学关系：推断变量之间因果关系的注意事项

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摘要

The present paper focuses on the relationship between latent change score (LCS) and autoregressive cross-lagged (ARCL) factor models in longitudinal designs. These models originated from different theoretical traditions for different analytic purposes, yet they share similar mathematical forms. In this paper, we elucidate the mathematical relationship between these models and show that the LCS model is reduced to the ARCL model when fixed effects are assumed in the slope factor scores. Additionally, we provide an applied example using height and weight data from a gerontological study. Throughout the example, we emphasize caution in choosing which model (ARCL or LCS) to apply due to the risk of obtaining misleading results concerning the presence and direction of causal precedence between two variables. We suggest approaching model specification not only by comparing estimates and fit indices between the LCS and ARCL models (as well as other models) but also by giving appropriate weight to substantive and theoretical considerations, such as assessing the justifiability of the assumption of random effects in the slope factor scores.
机译：本文重点研究了纵向设计中潜在变化得分（LCS）与自回归交叉滞后（ARCL）因子模型之间的关系。这些模型源自用于不同分析目的的不同理论传统，但它们具有相似的数学形式。在本文中，我们阐明了这些模型之间的数学关系，并表明当在斜率因子评分中假设固定效应时，LCS模型被简化为ARCL模型。此外，我们提供了一个使用老年医学研究中身高和体重数据的应用示例。在整个示例中，我们强调在选择使用哪种模型（ARCL或LCS）时要谨慎，因为存在关于两个变量之间因果优先级的存在和方向的误导性结果的风险。我们建议，不仅应通过比较LCS和ARCL模型（以及其他模型）之间的估计值和拟合指数，而且还应通过适当考虑实质性和理论性因素（例如评估随机假设假设的合理性）来接近模型规范。斜率因子得分。

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