The assessment of model fit is important in Structural Equation Modeling (SEM). Several goodness-of-fit (GoF) measures are affected by sample size and the number of parameters to be estimated. A large sample size is needed to test a complex model involving a large number of parameters to be estimated. One of the solutions to reduce the number of parameters to be estimated in a given model is by considering item parceling. The effects of item parceling on parameter estimates and GoF measures in a structural equation model was investigated via a simulation study. The simulation results indicate that the parameter estimates are closer to the true parameter values for the IL model whenever the distribution of data is normal but biased when the data is highly skewed. The parameter estimates for the IP model were found to be underestimated for both normal and non-normal data. The GoF measures were higher for the IP model. Additionally, the RMSEA was lower for the IP model when data were skewed. This shows that item parceling may improve GoF measures but the effect of exogenous on endogenous variable is underestimated. Application to a real data set confirmed the results of the simulation study.