Advanced methods for ALL tracks.
This course gives an introduction to some of the hottest topics in multivariate analysis. The approaches discussed do no try to build a simplistic model of how some variables may affect other ones, rather ask, whether, with an appropriate definition of these concepts, associations and effects (evidential or causal), exist. The principles and methods developed can be applied equally to survey and organic data.
Topics to be covered:
Effects and associations, Simpson's paradox Latent class analysis Data collection design and inference Causal analysis from observational studies Propensity score based matching Graphical models of causality Bayesian networks Analysis of higher order interactions Marginal models and path models