This three day course will teach you advanced topics in multilevel modelling. The three-day course builds upon the contents of the other summer school course “Introduction to multilevel analysis”. It consists of three days with lectures in the morning and computer labs in the afternoon. After taking this course, you should be able to analyse more complex multilevel models and to interpret and report the results.
The focus of the first day is on categorical outcome data, in particular binary, ordinal and event history outcomes. It will be shown why linear multilevel models are not appropriate for such data and how multilevel generalised linear models can be used to fit this type of outcome data. Attention will be paid to estimation procedures that are available and how the intraclass correlation coefficients and proportions explained variance are calculated. Special attention is paid to the interpretation of the estimated regression weights in terms of the logits and odds ratios. Analyses will be done in HLM and Mplus.
The focus of the second day is on multilevel factor analysis and multilevel structural equation modelling. The interest of such models is generally on theoretical constructs, which are presented by latent factors. It will be shown how to specify factor models at the between- and within-level and how to use fit indices to evaluate model fit. Path models consist of complex paths between latent and/or observed variables, possibly including direct and indirect effects. With multilevel path models, we often have the complication that there are different variables at the individual and group level. Mplus will be used to specify and fit such models.
The focus of day three is on random cross-classifications and statistical power analysis. An example of a random cross-classification is pupils nested within schools and neighborhoods. In this example a random effect should be included for schools and another one for neighborhoods, and the two may even covary. Such models can be fitted in HLM and special attention to the interpretation of results will be given. The aim of an a priori statistical power analysis is a calculation of sample size such that an effect can be detected with a sufficient probability. With a two-level model there are two sample sizes: the number of groups and the group size. For some simple experimental designs these sample sizes can be calculated on the basis of mathematical formulae and a demonstration of software will be given. For more complex designs, a simulation study has to be conducted to calculate sample size. It will be shown how to design such a simulation study and how to execute it in Mplus.
It is expected participants have taken the course Introduction to Multilevel Analysis or a similar course with the same content (i.e. chapters 1-5 from Hox, Moerbeek and Van de Schoot (2018)). Participants are also expected to have experience with analysing multilevel data in common software such as Mplus, SPSS, R, HLM, or MLwiN.
The book is not included in fee (about 45 euros): Hox, J., Moerbeek, M., & Van de Schoot, R. (2018). Multilevel analysis. Techniques and Applications. 3rd edition. New York: Routledge.
Participants are requested to bring their own laptop computer. Software will be available online
Please note that there is always the possibility that we have to change the course pending COVID19-related developments. The exact details, including a day-to-day program, will be communicated 6 weeks prior to the start of the course.
PhD students and researchers in the fields of social and behavioral sciences, medicine, health sciences, social geography. A maximum of 40 participants will be allowed in this course. The selection for this course will be done on a first-come-first-served basis.
For an overview of all our summer school courses offered by the Department of Methodology and Statistics please click here.
In the morning there will be lectures while during the afternoons multilevel analyses will be performed in computer practicals. Number of contact hours per day: 6. Number of self study hours per day: 2-3.
Please note that there are no graded activities included in this course. Hence, we cannot provide students with a transcript of grades. You will receive a certificate upon completion of the course.
You can choose between two options for participating in this course, but please note that there is always the possibility that we have to change the course pending COVID19-related developments:
- If you choose the livestream option, you will get a discount on the course fee since we will not provide lunch then. The lectures will be broadcasted in Central European Summer Time via a livestream (not recorded). Participants can ask questions via the chat which will be moderated by a second lecturer who will either directly answer your questions via the chat or ask your questions to the first lecturer during class. You will also receive online support during the group computer labs from our team. Additionally, Q&A sessions will be organised so you will benefit from our normal high level expertise while enjoying the class from the comfort of your own chair.
- If you choose the campus option, you will be able to attend the lectures and computer labs at our campus. Of course, we will follow all COVID19-guidelines that hold at the time of the start of your course. We will keep you updated about the newest developments (see also https://www.uu.nl/en/information-coronavirus).
If you are interested in the campus option, let us know via a message in the application form under ‘Student Comment’.
The physical course costs €510, but if you participate via the livestream you will get a 60 euro discount.
Tuition fee for PhD students from the Faculty of Social and Behavioural Sciences from Utrecht University will be funded by the Graduate School of Social and Behavioural Sciences.
There are no scholarships available for this course.
Irma Reyersen | E: firstname.lastname@example.org
For frequently asked questions about how we organize our summer school courses during the pandemic, please click here