The article briefly describes multilevel models and presents their simplest applications. After the methodological and statistical need for this procedure is explained, real data are used to demonstrate how a hierarchical linear model is constructed. The article presents models with a random intercept, models with random slopes, and models with explanatory variables measured at higher levels. In the conclusion, other possible applications of multilevel analysis are discussed, and the basic readings on multilevel analysis are presented.
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