Longitudinal Modeling with Randomly and Systematically Missing Data: A Simulation of Ad Hoc, Maximum Likelihood, and Multiple Imputation Techniques

For organizational research on individual change, missing data can greatly reduce longitudinal sample size and potentially bias parameter estimates. Within the structural equation modeling framework, this article compares six missing data techniques (MDTs): listwise deletion, pairwise deletion, stochastic regression imputation, the expectation-maximization (EM) algorithm, full information maximization likelihood (FIML), and multiple imputation...