In this final chapter, we examine SEM as it applies to multiple models drawn from a single data set. In contrast to the multigroup models illustrated in Part 3 that involved data from separate data sets, the multigroup model explored in this chapter is based on a single population that is hierarchically structured. Such models are termed multilevel because the data lend themselves to more than one level of analyses. The most common examples of hierarchically structured data are (a) students nested within schools, (b) patients nested within clinics, (c) employees nested within organizations, (d) children and adults nested within families, and the like. Such hierarchical structures are often termed nested data or clustered data. In essence, however, hierarchical structures may involve more than two levels of analysis. Building on these previous examples, data could be extended to include schools nested within districts and districts nested within states, or employees nested within departments, departments nested within organizations, organizations nested within regions, and so on. Beyond these examples, however, Hox (2002) showed that hierarchical structures also can include repeated measures within individuals or respondents within clusters, as in cluster sampling.