Complex datasets require more advanced analysis than simple regressions. Multilevel models, also known as hierarchical linear models, can analyze parameters that vary at different levels. These models are commonly used on data with multiple levels, with 2-level models being the most popular. In R, various packages in CRAN can estimate multilevel models. This paper presents common methods for analyzing these models.