Full Information Maximum Likelihood Estimation for Latent Variable Interactions With Incomplete Indicators.
Abstract
Researchers have developed missing data handling techniques for estimating interaction effects in multiple regression. Extending to latent variable interactions, we investigated full information maximum likelihood (FIML) estimation to handle incompletely observed indicators for product indicator (PI) and latent moderated structural equations (LMS) methods. Drawing on the analytic work on missing data handling techniques in multiple regression...
Paper Details
Title
Full Information Maximum Likelihood Estimation for Latent Variable Interactions With Incomplete Indicators.
Published Date
Jan 1, 2017
Volume
52
Issue
1
Pages
12 - 30
Citation AnalysisPro
You’ll need to upgrade your plan to Pro
Looking to understand the true influence of a researcher’s work across journals & affiliations?
- Scinapse’s Top 10 Citation Journals & Affiliations graph reveals the quality and authenticity of citations received by a paper.
- Discover whether citations have been inflated due to self-citations, or if citations include institutional bias.
Notes
History