Full Information Maximum Likelihood Estimation for Latent Variable Interactions With Incomplete Indicators.

Volume: 52, Issue: 1, Pages: 12 - 30
Published: Jan 1, 2017
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
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