Leveraging Process Data and Variable Selection for Achievement Estimation in Large‐Scale Assessments

Abstract The operational scaling in large‐scale assessments (LSAs) comprises two stages: item response theory and latent regression modeling. Principal component analyses (PCA) are routinely performed before latent regression modeling for dimension reduction, but this approach retains too many principal components (PCs), threatening numerical stability. This study proposes a new approach: adding process variables to the usual contextual variables and replacing PCA with variable selection for latent regression modeling. We found that using Lasso, random forests, and ultimately gradient boosting for variable selection led to measurement precision similar to or higher than the PCA approach, but with considerably fewer covariates; including process variables into latent regression models consistently improved measurement precision. Integrating process data and variable selection yielded the highest measurement precision while achieving parsimony: The latent regression models with the 150 most important variables, including process variables, outperformed those with 330 PCs in France and 270 PCs in the Republic of Korea. The findings suggest that our proposed approach can effectively solve the overparameterization problem in LSA scaling while preserving measurement precision.