M11-Multilevel Analysis for Grouped and Longitudinal Data
AI en Data Science
AI en Data Science
Social research often concerns relationships between individuals and the social contexts to which they belong. This can be conceptualized as a hierarchical structure, with individuals nested within groups.
Classical examples are educational research, with pupils nested within schools, and cross-national research, with individuals nested within their national units. They involve two level data: group level and individual level variables.
We need multilevel modeling to study the relationships between variables observed at different levels in the hierarchical structure.
This can also cover longitudinal research, by viewing measurement occasions as nested within respondents, and extends to situations where data have a more complex multilevel structure, such as cross-classified data or multiple-membership models.
This short course is intended as a basic and nontechnical introduction to multilevel analysis.
It starts with a description of some examples, and shows why multilevel models are necessary if the data have a hierarchical structure. It then covers the basic theory of two- and three-level models.
Next it explains how multilevel models can be applied to analyzing longitudinal data, and why and when this may be an attractive analysis approach, as compared to more classical analysis methods such as multivariate analysis of variance (Manova).
The course includes three computer labs, where multigroup and longitudinal data are analyzed. The computer labs in the course use the multilevel program HLM as a didactic analysis tool in combination with SPSS, a free environment for statistical computing and graphics.
Fees and registration form are available on the website of the Academy for Lifelong Learning of the Faculty of Sciences (UGent).
This course targets professionals and investigators from diverse areas ranging from researchers in the behavioral and social sciences to whoever deals with data with a hierarchical or multilevel structure.
The course assumes reasonable familiarity with analysis of variance (as eg. taught in Module 5 of this year's program) and multiple regression analysis, but prior knowledge of multilevel modeling is not assumed.
If you take part in all 6 sessions you will receive a certificate of attendance via e-mail after the course ends.
Additionally, you can take part in an exam. If you succeed in this test a certificate from Ghent University is issued.
The exam consists of a take home project assignment. You are required to write a report by a set deadline.
This is an on campus course. We offer blended learning options if, exceptionally, you can't attend a session on campus.
Three consecutive full days during the Easter Holiday: Wednesday April 12, Thursday April 13 and Friday April 14, 2023 from 9 am till 4 pm.
Faculty of Science, Campus Sterre, Krijgslaan 281, building S9, Ghent
Access to course notes and data files
The course is based on: J.J. Hox and M. Moerbeek (2017), "Multilevel Analysis. Techniques and Applications", 3rd edition, New York: Routledge, ISBN 978-1138121362. Those who wish to explore this topic further after the course can order it online or at their local book store. Please note that you do not need a copy of this book to follow the course.