Multilevel models, also known as mixed models are used to analyse correlated observations. Correlated observations can occur, for instance, when subjects are clustered within neighbourhoods, patients are clustered within hospitals, students are clustered within schools, etc. Besides this, correlated observations also occur in longitudinal studies where the repeated measurements over time are clustered within each individual. Multilevel analysis provides a very elegant and powerful tool to deal with this clustering, i.e. to deal with correlated observations. For longitudinal data analysis, besides multilevel analysis, also other methods, such as GLM for repeated measures and generalised estimating equation (GEE) analysis, are available.<\/p>\n
This six-day course will explain the basic concepts of multilevel analysis, some specific application of multilevel analysis and will further focus on longitudinal data analysis. The latter includes standard modelling, alternative modelling and the analysis of RCT data. It is an applied course, so the emphasis lies on the interpretation of the results from the different analyses and not on the mathematical background. Lectures are given in the morning and in the afternoon a computer practical is given using the statistical programs STATA, SPSS and R.<\/p>\n","protected":false},"template":"","class_list":["post-3269","cursus","type-cursus","status-publish","hentry"],"acf":[],"yoast_head":"\n