Although the interest in latent growth models (LGMs) with categorical indicator variables has recently increased, there are still difficulties regarding the selection of estimation methods and the interpretation of model estimates. However, difficulties in estimating and interpreting categorical LGMs can be avoided by understanding the scaling process. Depending on which parameter constraint methods are selected at each step of the scaling process, the scale applied to the model changes, which can produce significant differences in the estimation results and interpretation. In other words, if a different method is chosen for any of the steps in the scaling process, the estimation results will not be comparable. This study organizes the scaling process and its relationship with estimation methods for categorical LGMs. Specifically, this study organizes the parameter constraint methods included in the scaling process of categorical LGMs and extensively considers the effect of parameter constraints at each step on the meaning of estimates. This study also provides evidence for the scale suitability and interpretability of model estimates through a simple illustration. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

中文翻译：

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使用分类指标变量对潜在增长模型进行缩放和估计。


尽管最近人们对带有分类指标变量的潜在增长模型（LGM）的兴趣有所增加，但在估计方法的选择和模型估计的解释方面仍然存在困难。然而，通过理解缩放过程可以避免估计和解释分类 LGM 的困难。根据在缩放过程的每个步骤中选择的参数约束方法，应用于模型的缩放会发生变化，这可能会在估计结果和解释中产生显着差异。换句话说，如果缩放过程中的任何步骤选择不同的方法，则估计结果将不具有可比性。本研究组织了缩放过程及其与分类 LGM 估计方法的关系。具体来说，本研究组织了分类LGM缩放过程中包含的参数约束方法，并广泛考虑了每一步参数约束对估计意义的影响。这项研究还通过一个简单的说明，为模型估计的规模适用性和可解释性提供了证据。 （PsycInfo 数据库记录 (c) 2024 APA，保留所有权利）。