Multilevel modeling is the simultaneous use of more than one source of data in a hierarchical structure of units and is useful for analysis of clustered or longitudinal data. Single-level models are unable to accommodate the characteristics of hierarchical data structures, such as unit of analysis, aggregation bias, state dependency, and within-group and between-group heterogeneity, resulting in misestimations. By accommodating these characteristics, multilevel modeling allows researchers to use data more fully and efficiently and to assess the direction and magnitude of relationships within and between contextual and individual factors.

In the past few years, multilevel modeling, also known as hierarchical linear models, mixed-effects models, random-effects models, and random-coefficient models, has become increasingly common in public health research, partly due to growing interest in social determinants ...

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