Using Machine Learning to Validate Factor-Analytic Strategies for Creating Abbreviated Measures

The efficacy of factor analysis and machine learning approaches in assessing item importance was tested using a GAD screening measure. Item factor loadings showed high agreement with item importance from machine learning models. Retaining items with higher factor loadings is effective for creating internally valid and clinically useful abbreviated measures.