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Multilevel Models
Multilevel models

In many applied contexts, measurements show a form of dependency among the observations known as clustering: for example, pupils within classrooms, employees within workplaces, or patients in treatment centres. Multilevel models distinguish between the effects of variables at different levels of nesting and allow the total variation in the response variable to be partitioned into between- and within-cluster variance components (e.g. Snijders and Bosker, 1999). This involves exploring relevant variables and estimating how the response variable varies as each explanatory variable changes in value, taking into account the other variables also in the model. Multilevel models differ from ordinary least-squares (OLS) regression models in that the former contain a random component at each level. Checks made during model building allow the researcher to ...

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