<![CDATA[Hierarchical data is data that has a multilevel structure, where individuals belong to certain groups and variables are measured at different levels. Analysis of hierarchical data without considering group membership may result in bias due to violation of the independence assumption. Multilevel regression analysis is a statistical method to overcome this problem by modeling the data structure at several levels. This study aims to apply three-level multilevel regression analysis to identify factors that affect the study duration of undergraduate students at FMIPA Udayana University. In this data structure, students are at the first level, grouped into classes at the second level, and the classes are in study programs at the third level. The model uses random intercepts as well as exploring random slopes to assess differences in the influence of independent variables between study programs.]]>
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