The Effect of Modeling Missing Data With IRTree Approach on Parameter Estimates Under Different Simulation Conditions.

ABSTRACT: This study explores the performance of the item response tree (IRTree) approach in modeling missing data, comparing its performance to the expectation-maximization (EM) algorithm and multiple imputation (MI) methods. Both simulation and empirical data were used to evaluate these methods across different missing data mechanisms, test lengths, sample sizes, and missing data proportions. Expected a posteriori was used for ability estimation, and bias and root mean square error (RMSE) were calculated. The findings indicate that IRTree provides more accurate ability estimates with lower RMSE than both EM and MI methods. Its overall performance was particularly strong under missing completely at random and missing not at random, especially with longer tests and lower proportions of missing data.

SUBMITTER: Soguksu YB 

PROVIDER: S-EPMC11669122 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature