Student mobility exists, but it’s not always taken into account in value-added modeling approaches used to determine school accountability rankings. Multiple membership modeling can account for student mobility in a multilevel framework, but it is more computationally demanding and requires specialized knowledge and software packages that may not be available in state and district departments of education. The purpose of this dissertation was to compare how different multilevel value-added modeling approaches perform at various levels of mobility to be able to provide recommendations to state- and district-administrators about the type of models that would be best suited to their data. To accomplish this task, a simulation study was conducted, manipulating the percentage of mobility in the dataset and the similarity of the sender and receiver schools of mobile students. Traditional gains score and covariate adjustment models were run, along with comparable multiple membership models to determine the extent to which school effect estimates and school accountability rankings were affected and to investigate the conditions under which a multiple membership model would produce a meaningful increase in accuracy to justify its computational demand. Additional comparisons were made on measures of relative bias of the fixed effect coefficients, the random effect variance components, and the relative bias of the standard errors of the fixed effects and random effects variance components. The multiple membership models with schools proportionally weighted by time spent were considered better fitting models across all conditions. All multiple membership models were able to better recover the intercept and school-level residual variance better than other models. However, when considering school accountability rankings, the proportion of school quintile shifts was close to equal across the traditional and multiple membership models that were structurally similar to each other. This finding suggests that the use of a multiple membership model is preferable in providing the most accurate parameter and standard error estimates. However, if school accountability rankings are of primary interest, a traditional VAM performs equally as well as a multiple membership model. An empirical data analysis was conducted to demonstrate how to prepare data and properly run these various models and how to interpret the results, along with a discussion of issues to consider when selecting a model. Recommendations are provided on how to select a model, informed by the findings from the simulation portion of the study.