![]() Longitudinal data are also clustered, with repeated measurements on individuals or multiple panel waves per survey respondent. ![]() Special interest lies in disentangling social processes operating at different levels of analysis by decomposing the within- from the between-cluster effects of covariates (explanatory or predictor variables). Such models are appropriate when, for example, analysing exam scores of students nested within schools, or health outcomes of patients nested within hospitals. These models can be viewed as an extension of conventional linear and logistic regression models to account for and learn from the clustering in the data. We focus on multilevel modelling for continuous and binary responses (dependent or outcome variables) when the data are clustered (nested or hierarchical). ![]() This three-day course provides an introduction to multilevel modelling and includes software practicals in your choice of software: MLwiN, R, or Stata. Professor George Leckie and Professor William Browne Introduction to Multilevel Modelling Using MLwiN, R, or Stata ![]()
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