The effectiveness of Stout's procedure for assessing latent trait unidimensionality was studied. Strong empirical evidence of the utility of the statistical test in a variety of settings is provided. The procedure was modified to correct for increased bias, and a new algorithm to determine the size of assessment sub-tests was used. The following two issues were addressed via a Monte Carlo simulation: (1) the ability to approximate the nominal level of significance via the observed level of significance; and (2) the power of the statistical test while undergoing the modifications. Results indicate that Stout's statistic and the procedure, which grew directly out of a meaningful conceptual definition of test dimensionality, avoids the issue of parametric model correctness, attracts the support of asymptotic theory, has modest computational requirements, and receives support from Monte Carlo simulations. (TJH)