Abstract
ABSTRACT Integrative data analysis (IDA) involves obtaining multiple datasets, scaling the data to a common metric, and jointly analyzing the data. The first step in IDA is to scale the multisample item-level data to a common metric, which is often done with multiple group item response models (MGM). With invariance constraints tested and imposed, the estimated latent variable scores from the MGM serve as an observed variable in subsequent analyses. This approach was used with empirical multiple group data and different latent variable estimates were obtained for individuals with the same response pattern from different studies. A Monte Carlo simulation study was then conducted to compare the accuracy of latent variable estimates from the MGM, a single-group item response model, and an MGM where group differences were ignored. Results suggest that these alternative approaches led to consistent and equally accurate latent variable estimates. Implications for IDA are discussed.
Sign-in/Register to access full text options 
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.
