Subject description
1. The idea of multilevel modelling
- Sources of clustered data.
- Multilevel Theories.
2. Two level models
- Linear random intercept model.
- Linear random slopes model.
3. Three level variance component model
4. Cross level coefficients
5. Logistic random coeficient models
6. Logistic three level models
The subject is taught in programs
Objectives and competences
The students will upgrade the existing knowledge of the dependence models by dropping the assumption of independent observations. They will learn how to model hierarchically ordered data. After completing the course the students will:
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independently recognize hierarchically ordered data,
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form an appropriate model and
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estimate it using statistical software
Teaching and learning methods
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Lectures.
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Seminars.
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Computer labs.
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Consultations.
Expected study results
The studets will learn about the consequences of dropping the assumption of independent observation and addequatly model data of clustered observation with dependencies arrising at different levels.
Basic sources and literature
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Snijders, Tom A.B., and Bosker, Roel J. Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling, second edition. London etc.: Sage Publishers, 2012.
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Sophia Rabe-Hesketh and Anders Skrondal. Multilevel and Longitudinal Modelling Using Stata, 2nd Edition, Stata Press, 2008.
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University of Ljubljana, Faculty of Electrical Engineering Tržaška cesta 25, 1000 Ljubljana