Hierarchical organization of schooling in all nations insures that international large-scale assessment data are multilevel where students are nested within schools and schools are nested within nations. Longitudinal follow-up of these students adds an additional level. Hierarchical or multilevel models are appropriate to analyze such data. A ubiquitous problem, however, is that explanatory as well as outcome variables may be subject to missingness at any of the levels, posing the data analyst with a challenge. This chapter explains how to efficiently analyze a two-level hierarchical linear model given incompletely observed data where students at level 1 are nested within schools at level 2. This social setting may also apply to occasions nested within individuals, students nested within nations, and schools nested within nations. The efficient missing data method we use in this chapter aims to analyze all available data. The "all available data" include children with item as well as unit nonresponse as they belong to a school and a nation having observed data and thus add information to strengthen inferences at higher levels. [This chapter was published in: Rutkowski, L., von Davier, M., & Rutkowski, D. (Eds.), "Handbook of International Large-Scale Assessment: Background, Technical Issues, and Methods of Data Analysis" (pp. 451-479). New York, NY: CRC Press.]