This study compared three approaches for handling a fourth level of nesting when analyzing cluster-randomized trial (CRT) data. Although CRT data analyses may include repeated measures, individual, and cluster levels, there may be an additional fourth level that is typically ignored. This study examined the impact of ignoring this fourth level, accounting for it using a model-based approach, and accounting for it using a design-based approach on parameter and standard error ("SE") estimates. Several fixed effect and random effect variance parameters and "SE"s were biased across all three models. Results suggest if a meaningful fourth level exists, researchers should acknowledge it using a design-based approach. If the fourth level is not practically important, researchers may ignore it altogether, resulting in more accurate parameter and "SE" estimates.