Least absolute deviation (LAD) is a well-known criterion to^{ }fit statistical models, but little is known about LAD estimation^{ }in structural equation modeling (SEM). To address this gap,^{ }the authors use the LAD criterion in SEM by minimizing the sum^{ }of the absolute deviations between the observed and the model-implied^{ }covariance matrices. Using Monte Carlo simulations, the authors^{ }compare the performance of this LAD estimator along several^{ }dimensions (bias, efficiency, convergence, frequencies of improper^{ }solutions, and absolute percentage deviation) to the full informationmaximum likelihood (ML) and unweighted least squares (ULS) estimators^{ }in structural equation modeling. The results for LAD are mixed:^{ }There are special conditions under which the LAD estimator outperforms^{ }ML and ULS, but the simulation evidence does not support a general^{ }claim that LAD is superior to ML and ULS in small samples.

**Key Words:** least absolute deviation • structural equation modeling • robust estimation • small sample research