This article compares maximum likelihood (ML) estimation to^{ }three variants of two-stage least squares (2SLS) estimation^{ }in structural equation models. The authors use models that are^{ }both correctly and incorrectly specified. Simulated data are^{ }used to assess bias, efficiency, and accuracy of hypothesis^{ }tests. Generally, 2SLS with reduced sets of instrumental variables^{ }performs similarly to ML when models are correctly specified.^{ }Under correct specification, both estimators have little bias^{ }except at the smallest sample sizes and are approximately equally^{ }efficient. As predicted, when models are incorrectly specified,^{ }2SLS generally performs better, with less bias and more accurate^{ }hypothesis tests. Unless a researcher has tremendous confidence^{ }in the correctness of his or her model, these results suggest^{ }that a 2SLS estimator should be considered.

**Key Words:** 2SLS • misspecification • latent variable models • structural equation models • FIML • specification error