A Random Forest Threshold Imputation Method for Handling Missing Data in Cognitive Diagnosis Assessments

You Xiaofeng ¹ Yue Xiao ² Jianqin Yang ¹ Hongyun Liu ^3*

• ¹ Nanchang Normal University, Nanchang, China
• ² Department of Educational Psychology, Facutly of Education, East China Normal University, Shanghai, China
• ³ Faculty of Psychology, Beijing Normal University, Beijing, Beijing, China

The handling of missing data in cognitive diagnostic assessment is an important issue. You et al. (2023) proposed a Random Forest Threshold Imputation (RFTI) method specifically designed for cognitive diagnostic models (CDMs) and built on the random forest imputation. However, in RFTI, the threshold for determining imputed values to be 0 is fixed at 0.5, which may result in uncertainty in this imputation. To address this issue, we proposed an improved method, Random Forest Dynamic Threshold Imputation (RFDTI), which possess two dynamic thresholds for dichotomous imputed values. A simulation study showed that the classification of attribute profiles when using RFDTI to impute missing data was always better than the four commonly used traditional methods (i.e., person mean imputation, two-way imputation, expectation-maximization algorithm, and multiple imputation). Compared with RFTI, RFDTI was slightly better for MAR or MCAR data, but slightly worse for MNAR or MIXED data, especially with a larger missingness proportion. An empirical example with MNAR data demonstrates the applicability of RFDTI, which performed similarly as RFTI and much better than the other four traditional methods. An R package is provided to facilitate the application of the proposed method.