Factors associated with success in medical school: systematic review of the literature

Hierarchical linear modelling
 
 

References retrieved by search

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Hierarchical linear modelling

Hierarchical linear modelling is a statistical procedure that allows for natural hierarchies within data to be taken into account in analysis. A natural hierarchy, for example, might be patients, who are hierarchically nested within general practices; the general practices are hierarchically nested within health authorities. In this example, patients are said to be at level 1 in the hierarchy, general practices at level 2, and health authorities at level 3. Data pertaining to each level can be collected. At level 1 this might be diet, smoking behaviour, blood pressure, and cholesterol; at level 2 it might be the ratio of patients to general practitioners, size of practice, prescription habits, practice facilities, and sociodemographics of each practice; and level 3 data could be geographical region and funding. Hierarchical linear modelling allows the researcher to explore how variables at one level in the hierarchy influence variables at lower levels. For example, are cholesterol levels best predicted by diet (level 1), the type of practice (level 2), or the health authority region (level 3)? It allows for the maximum power in the data to be used, mathematically incorporating the inherent hierarchical nature of the data, and examines for influences across levels within the hierarchy.