Comparing Bayesian estimation and structural-after-measurement approaches for structural equation models with latent interactions and complex data structures.

ABSTRACT: Bayesian and structural-after-measurement (SAM) approaches have been developed, in part, to address limitations of conventional estimators in the context of structural equation models (SEMs) with latent interactions. Although both approaches have shown promise in a variety of contexts including small-sample studies, there is very little literature systematically comparing the relative benefits, limitations, and trade-offs among these approaches. In this study, we (a) compared the performance of estimators under each approach in multilevel SEMs with a within-, between-, or cross-level latent interaction and (b) demonstrated the flexibility of SAM approaches by extending and investigating them in partially nested SEMs with latent moderated mediation. The results suggest substantial differenc

SUBMITTER: Cox K 

PROVIDER: S-EPMC12546545 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature