SEM is often preferred over multiple regression due to the many statistical and conceptual advantages. One of these is its ability to account for measurement error and obtain unbiased estimates of the relationships between latent variables. This decrease in bias does not come without cost as SEM suffers from decreased precision of parameter estimates as compared to multiple regression. Reduced precision is a consequence of increased model complexity as well as the increased effect of collinearity due to the dissattenuation of the correlation between latent predictors. This paper examines the bias, precision, accuracy, and confidence interval coverage of parameter estimates in SEM and multiple regression. Results show that with small sample sizes multiple regression can often produce parameter estimates that are more accurate than SEM even though the estimates are biased. Multiple factors that affect the accuracy of estimates are explored and some suggestions are provided for researchers.