Many national and international educational data collection programs offer researchers opportunities to investigate contextual effects related to student performance. In those programs, schools are often used in the first-stage sampling process and students are randomly drawn from selected schools. However, the "incidental" dependence of students within classrooms, which are not part of the sampling design, may violate assumptions of statistical models, but this nesting also offers the opportunity for educational researchers to evaluate contextual effects. In this manuscript, we utilize the Early Childhood Longitudinal Study-Kindergarten dataset to demonstrate impacts of incidental dependence using a two-level model and a three-level model. We then illustrate, through a simulation, that both models can yield unbiased parameter estimates. However, two-level models tend to provide underestimated standard errors for fixed effects at the incidental level, and variance components of the random effect at the incidental level are divided into the flanking levels when it is ignored. In addition, another method of modeling nested data, using generalized estimating equations, was also compared with the model-based methods.