The Effects of Missing Data Handling Methods on Reliability Coefficients: A Monte Carlo Simulation Study

Abstract

This study holds significant implications as it examines the impact of different missing data handling methods on the internal consistency coefficients. Using Monte Carlo simulations, we manipulated the number of items, true reliability, sample size, missing data ratio, and mechanisms to compare the relative bias of reliability coefficients. The reliability coefficients under scrutiny in this study encompass Cronbach's Alpha, Heise & Bohrnsted's Omega, Hancock & Mueller's H, Gölbaşı-Şimşek & Noyan's Theta G, Armor's Theta, and Gilmer-Feldt coefficients. Our arsenal of techniques includes single imputation methods like zero, mean, median, and regression imputation, as well as multiple imputation approaches like expectation maximization and random forest. We also employ the classic deletion method known as listwise deletion. The findings suggest that, for missing completely at random (MCAR) or missing at random (MAR) data, single imputation approaches (excluding zero imputation) may still be preferable to expectation maximization and random forest imputation, thereby underscoring the importance of our research.