The term ‘Multilevel Analysis’ is mostly used interchangeably with ‘Hierarchical Linear Modeling’, although strictly speaking these terms are distinct. Multilevel Analysis may be understood to refer broadly to the methodology of research questions and data structures that involve more than one type of unit. This originated in studies involving several levels of aggregation, such as individuals and counties, or pupils, classrooms, and schools. Starting with Robinson’s (1950) discussion of the ecological fallacy, where associations between variables at one level of aggregation are mistakenly regarded as evidence for associations at a different aggregation level (further see Alker, H.R.: A typology of ecological fallacies. In: Dogan, M., Rokkan, S. (eds.) Quantitative Ecological Analysis in the Social Sciences, pp. 69–86. The M.I.T. Press, Cambridge, 1969 and Gnaldi et al. Int Stat Rev86:119–135, 2018), this led to interest in how to analyze data including several aggregation levels. This situation arises as a matter of course in educational research, and studies of the contributions made by different sources of variation such as students, teachers, classroom composition, school organization, etc., were seminal in the development of statistical methodology in the 1980s (see the review in Chapter 1 of de Leeuw and Meijer: Handbook of Multilevel Analysis. Springer, New York, 2008). The basic idea is that studying the simultaneous effects of variables at the levels of students, teachers, classrooms, etc., on student achievement requires the use of regression-type models that comprise error terms for each of those levels separately; this is similar to mixed effects models studied in the traditional linear models literature such as Scheffé: The Analysis of Variance. Wiley, New York, 1959.