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Regularization methods in linear regression models with manifest variables have been shown to be effective in selecting key predictors from a set of many variables, while improving predictions for novel observations. Regularization methods are particularly attractive for the analysis of complex multidimensional data when theory development is the primary goal; for example when researchers attempt to predict general or specific factors in bifactor models using many potentially relevant predictors. However, applications of regularization methods in such models are still scarce. In a simulation study, we examined the performance of different regularization methods in bifactor-(S-1) models, varying the number of predictors, the correlations with the outcome (effect size), the underlying structure of multicollinearity as well as the sample size, the type of penalty, and a single-step versus a two-step approach. We explore potential caveats in the use of regularization methods in bifactor-(S-1) models, provide practical recommendations, and discuss future directions.

structural equation modeling: a multidisciplinary journal

On the Performance of Different Regularization Methods in Bifactor-(S-1) Models with Explanatory Variables—Caveats, Recommendations, and Future Directions