A General Theorem and Proof for the Identification of Composed CFA Models.

ABSTRACT: In this article, we present a general theorem and proof for the global identification of composed CFA models. They consist of identified submodels that are related only through covariances between their respective latent factors. Composed CFA models are frequently used in the analysis of multimethod data, longitudinal data, or multidimensional psychometric data. Firstly, our theorem enables researchers to reduce the problem of identifying the composed model to the problem of identifying the submodels and verifying the conditions given by our theorem. Secondly, we show that composed CFA models are globally identified if the primary models are reduced models such as the CT-C[Formula: see text] model or similar types of models. In contrast, composed CFA models that include non-reduced primary

SUBMITTER: Bee RM 

PROVIDER: S-EPMC10656336 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature