A Monte Carlo study was conducted to assess the effects of some potential confounding factors on structural equation modeling (SEM) fit indices and parameter estimates for both true and misspecified models. The factors investigated were data nonnormality, SEM estimation method, and sample size. Based on the fully crossed and balanced 3x3x4x2 experimental design with 200 replications in each cell division, a total of 14,400 samples were generated and fitted to SEM models with different degrees of model misspecification. The major findings are: (1) mild to moderate data nonnormality has little effect on SEM fit indices and parameter estimates; (2) estimation method has considerable influence on some SEM fit indices when the model was misspecified, primarily on those comparative model fit indices; and (3) some fit indices are susceptible to the influence of sample size, and show moderate downward bias under smaller sample size conditions. Previous studies in this area have simulated a correctly-specified true model, and fit indices were found to behave consistently under different estimation methods. That finding may need to be assessed again, because considerable discrepancy of some fit indices between the two estimation methods was observed for misspecified models. It is critical that simulation studies be conducted in the presence of model misspecification. (Contains 1 figure, 8 tables, and 54 references.) (Author/SLD)