This article critiques traditional single-level statistical approaches (e.g., multiple regression analysis) to examining relationships between language test scores and variables in the assessment setting. It highlights the conceptual, methodological, and statistical problems associated with these techniques in dealing with multilevel or nested data and discusses an alternative approach, multilevel modeling (MLM), that can handle such data appropriately. An example focusing on contrast effects in essay rating is used to illustrate the main points discussed in the paper and the applications and advantages of MLM. The article also discusses some of the main considerations and issues in MLM (e.g., model building and testing, centering) and concludes by pointing out areas where MLM can be applied in language assessment research.