*연구과제명: Evaluation of Generative Adversarial Imputation Nets’ Performance in Handling Missing Data in Structural Equation Modeling
-연구 기관: McGill University and Sogang University (연구팀)
-저자: Luqi He, Yingke Lu, Carl F. Falk, and Heungsun Hwang
Missing data are a common challenge in structural equation modeling (SEM), potentially leading to biased estimates and reduced power. Full information maximum likelihood (FIML) and multiple imput ation (MI) are widely used to address this issue. Recently, generative adversarial imputation nets (GAIN), a machine learning–based method, have shown promise under high missingness; however, their utility within SEM contexts remains largely unexplored. This simulation study compared GAIN, FIML, and MI across several experimental factors. Under correct model specification, all methods yielded comparable estimates, with GAIN exhibiting greater variability and poorer recovery of model fit. Under model misspecification, performance differences became more pronounced with increasing missingness. Below 50%, all methods performed similarly, though GAIN showed higher variability and poorer fit recovery. At 50%, MI and GAIN outperformed FIML with comparable accuracy. At 75%, GAIN produced more accurate estimates than MI but continued to show the greatest variability and poorest model fit recovery.
*연구과제명: Evaluation of Generative Adversarial Imputation Nets’ Performance in Handling Missing Data in Structural Equation Modeling
-연구 기관: McGill University and Sogang University (연구팀)
-저자: Luqi He, Yingke Lu, Carl F. Falk, and Heungsun Hwang
Missing data are a common challenge in structural equation modeling (SEM), potentially leading to biased estimates and reduced power. Full information maximum likelihood (FIML) and multiple imput ation (MI) are widely used to address this issue. Recently, generative adversarial imputation nets (GAIN), a machine learning–based method, have shown promise under high missingness; however, their utility within SEM contexts remains largely unexplored. This simulation study compared GAIN, FIML, and MI across several experimental factors. Under correct model specification, all methods yielded comparable estimates, with GAIN exhibiting greater variability and poorer recovery of model fit. Under model misspecification, performance differences became more pronounced with increasing missingness. Below 50%, all methods performed similarly, though GAIN showed higher variability and poorer fit recovery. At 50%, MI and GAIN outperformed FIML with comparable accuracy. At 75%, GAIN produced more accurate estimates than MI but continued to show the greatest variability and poorest model fit recovery.