Abstract
This study aims to estimate the latent interaction effect in the CLPM model through a two-step multiple imputation analysis. The estimation of within × within and between × within latent interaction under the CLPM model framework is compared between the one-step Bayesian LMS method and the two-step multiple imputation analysis through a simulation study. The analysis show that the two-step multiple imputation analysis can provide unbiased estimation parameter, similar to the one-step Bayesian LMS method. This study also uses self-esteem and depression data from NLSY79 to perform a two-step multiple imputation analysis of the CLPM model, as well as an empirical example of latent interaction analysis. Mplus syntax is provided for researchers’ reference.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Structural Equation Modeling: A Multidisciplinary Journal
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
