<ul><li>Hierarchical Linear Models (HLM), also known as multilevel modeling, are statistical techniques used to analyze data that has a hierarchical or nested structure.</li><li>HLM involves at least two levels: individual observations and group-level influences, with a mathematical representation that considers predictors, random effects, and error terms.</li><li>Assumptions of HLM include normality of residuals, homoscedasticity, and independence of observations, with random effects following a normal distribution.</li><li>HLM is widely used in education, psychology, healthcare, and social sciences due to its ability to handle unbalanced data structures, incorporate fixed and random effects, and model complex real-world scenarios.</li></ul>

Hierarchical Linear Models (HLM), also known as multilevel modeling, are statistical techniques…

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