In educational research, data very often have a clustered structure, i.e., individuals belongs to groups. For example, students are nested classrooms and classrooms are nested in schools. In such a data hierarchy, students in the same classroom and school are much more similar than those from a different one. An appropriate way to acknowledge and analyze such dependencies in hierarchically clustered data is to apply a multilevel analytical technique.
This course will provide the participants with some basics of theoretical principles and essential methodological and statistical issues of the multilevel analysis techniques. We will touch upon both hierarchical linear model (HLM) and multilevel structural equation modelling (MSEM). The course also aims to equip the participants with the knowledge that enables a critical review of educational research using multilevel modelling techniques.