Despite the prevalence of ordinal observed variables in applied structural equation modeling (SEM) research, limited attention has been given to model evaluation methods suitable for ordinal variables, thus providing practitioners in the field with few guidelines to follow. This study represents a first attempt to thoroughly examine the performance of five fit measures --?? [superscript 2] statistic, Comparative Fit Index, Tucker-Lewis Index, Root Mean Square Error of Approximation, and Standardized Root Mean Square Residual--produced by robust weighted least squares (RWLS) estimators designed to accommodate ordinal and nonnormal observed variables, in Confirmatory Factor Analysis (CFA) model evaluation, under various realistic sample, data, and model conditions, especially when different types and degrees of model misspecification occur. Results showed that in evaluating the goodness-of-fit of CFA models with ordinal variables, fit measures produced by M"plus" WLSMV seemed to be more effective and reliable than those by LISREL DWLS across studied conditions. The WLSMV fit measures generally indicated good fit for the correct and mildly misspecified models and bad fit for the moderately misspecified models, provided that the model was not too large. The DWLS fit measures, on the other hand, were susceptible to influences of small samples and could be largely inflated or deflated when a small sample was used to evaluate a large model. In addition, single cut-off criteria for the fit indices are not possible, because all of the fit indices examined varied systematically with the size of the proposed model. Recommendations are made on practical issues pertaining to real-life CFA model evaluation with ordinal observed variables, such as minimum sample size required and how to use information provided by the RWLS fit measures to make model-data fit decisions, while taking into consideration the sample, data, and model characteristics specific to researchers' own studies.