Recent developments in multilevel modelling have made available to social scientists powerful statistical techniques for analyzing individuals as members of social groups. The techniques are also especially useful for repeated measures data
Multi-level models (MLM) are known by many synonyms (i.e., hierarchical linear models, general linear mixed models). The defining feature of these models is their capacity to provide quantification and prediction of random variance due to multiple sampling dimensions (across occasions, persons, or groups). Multilevel models offer many advantages for analyzing longitudinal data, such as flexible strategies for modeling change and individual differences in change, the examination of time-invariant or time-varying predictor effects, and the use of all available complete observations.
Multilevel models are also useful in analyzing clustered data (e.g., persons nested in groups), in which one wishes to examine predictors pertaining to individuals or to groups.
For inquires and support on Multi-Level Modelling in RISIS you can contact Barbara Antonioli Mantegazzini at the Università della Svizzera italiana in Lugano.
Due to the exceptional situation caused by Covid-19, the course on “Applications of multi-level models to research policy and higher education studies” (video soon available) was held with online classes on the 19th and 26th of October 2020.
All the participants had the opportunity to learn the fundamentals of this technique of data analysis. The course was structured with an initial part mostly dedicated to the description and investigation of the theoretical aspects of the methodology, while the second was focused on the exploitation of STATA coding. Different case studies focused on higher education have been presented, discussed and results reproduced and commented. Participants had the opportunity to work in groups developing their own case studies, in several cases shaped on their own ongoing research. Results have been presented and discussed with trainers as well as with other participants.