58: Bifactor Model Variants and Parameter Recovery in Multiple-Indicator Multiple Cause Modeling

Music City Center 

General(G)-factor latent variable models are used to characterize factor structure of unobserved variables. Bifactor and alternative bifactor models are common such models for G-factor based instruments. Despite their advantages for data structures with a dominant G factor, these models can produce anomalous findings. Thus, alternative bifactor models have been advanced to overcome these problems. There remains insufficient evidence regarding the degree to which such models recover known data structures, especially with model misspecification. This presentation reports a simulation study examining parameter recovery and model estimation convergence for a series of models, including: undimensional, bifactor, and bifactor (S-1) and (SI-1). Specific interest is recovery of the regression coefficient of a binary predictor on the primary dimension, especially in the presence of model misspecification, with a multiple-indicator multiple cause model. Manipulated conditions include: Sample size, factor correlation, number of indicators, and magnitude of group differences on primary and domain-specific latent means. The presentation includes full results and recommendations.

Latent variable modeling

Simulation study

Multiple-indicator multiple cause (MIMIC) modeling

G-Factor models

Parameter recovery 

Survey Research Methods Section