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 (MI). The rationale for each technique is reviewed, followed by Monte Carlo analysis based on a threewave simulation of organizational commitment and turnover intentions. Parameter estimates and standard errors for each MDT are contrasted with complete-data estimates, under three mechanisms of missingness (completely random, random, and nonrandom) and three levels of missingness (25%, 50%, and 75%; all monotone missing). Results support maximum likelihood and MI approaches, which particularly outperform listwise deletion for parameters involving many recouped cases. Better standard error estimates are derived from FIML and MI techniques. All MDTs perform worse when data are missing nonrandomly.