This thesis examines factors impacting item parameter calibration for the multidimensional graded response model in high dimensions (e.g., > 5) using Metropolis-Hastings Robbins-Monro estimation. Motivation for the use of high dimensional item response models and the associated computational difficulties are reviewed followed by an examination of existing work looking at item parameter recovery in the multidimensional graded response model. Research suggests that both test and trait characteristics, including potential non-normality of marginal trait distributions, interfere with efficient parameter recovery. The present study is the first to systematically examine the impact of marginal trait skewness and excess kurtosis on item parameter recovery. Specific focus is placed on identify necessary sample sizes to obtain accurate parameter estimates and the associated computational burden. Findings suggest that skewness and kurtosis impact different parameter estimates. Further, both sample size and computational burdens appear feasible for use in applied psychological research.