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Integrated generalized structured component analysis (IGSCA) has been developed for estimating structural equation models with both factors and components. In this study, we propose a regularized extension of IGSCA to address potential multicollinearity or perform automatic variable selection. This extension, termed Regularized IGSCA, aims to minimize a penalized least squares objective function that includes both L1 and L2 penalties. We present an empirical application to illustrate the usefulness of the method.

SEM; Machine Learning; Regularization