Scaling and estimation of latent growth models with categorical indicator variables.

Abstract

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).