Exploratory Measurement Modeling with Lasso
Date:
Kim, Y. & Sanders, E. A. (2023, July)
Algorithmic-based exploratory approaches for measurement modeling may be useful with large-scale survey and assessment data for which researchers have little theory to guide model selection. For example, the least absolute shrinkage and selection operator (Lasso), adaptive Lasso (aLasso), and minimax concave penalty (MCP) algorithms have been successfully applied within the IRT framework as well as within the SEM framework.
Using Monte Carlo simulation, we evaluated the performance (bias and variable factor matching) of different regularization methods (Lasso, aLasso, and MCP) in estimating factor loadings. (see one of the project slides)