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PhD Measurement and Assessment: PHDMAC730- Advanced Topics in Multilevel Modelling

Course Description

This course provides a comprehensive overview of multilevel models designed to deal with interactions between individual and contextual effects. Starting from exogenous microlevel data, individual effects are estimated first. Then, the estimated parameters are explained on the basis of characteristic features from the next higher hierarchical level. Point and interval estimators of the first and second hierarchical levels are derived. Regression estimation techniques (OLS, WLS, GLS) are described. There is also a presentation of Bayes estimation as well as full and restricted maximum-likelihood techniques for estimating variance and covariance components.


Peugh, J. L. (2010). A practical guide to multilevel modelling. Journal of School Psychology, 48(1), 85- 112.

Multilevel Modeling


Bickel, R. (2007). Multilevel analysis for applied research: It's just regression. Guilford

Garson, G. D. (2012). Hierarchical linear modelling: Guide and applications. Sage