Statistics

The course will be delivered over 16 weeks, as a blend of face-to-face small group work and lectures, practical exercises, formative assessments and answers and feedback.

It will cover the following subjects: Introduction to multilevel modeling (MLM), simple hierarchies, assumptions and consequence of ignoring hierarchy, Model specification, variance components (VC) model, random intercept, complex level 1, random slope, complex random slope; Model fit, residuals, diagnostics, model comparison, predictions, MCMC, Modeling other outcome distributions, binomial, Complex hierarchies, cross-classified, multiple-membership, Introduction to latent variables, principle components analysis (PCA), Exploratory (EFA) and confirmatory factor analysis (CFA), Causation and path analysis and Structural equation models (SEM).

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