G19 Research Methods for Multilevel DataInfo Location Contact More Info Event Information
DescriptionThis course is for anyone needing to analyse multilevel data. When your data are structured in nested groups (like students within classrooms, patients within hospitals, or individuals within regions), and you want to understand how factors at both levels influence the outcome variable, appropriate statistical techniques that account for this hierarchical structure should be used. The aim of the course is to provide an introduction to the analysis of multilevel data and to the selection of appropriate multilevel models based on your research questions, including whether to allow intercepts or slopes to vary across groups (random effects vs. fixed effects). Practical examples of how to successfully fit multilevel models and interpret results will be presented, using the statistical package Stata. There will be 6 sessions taught via Zoom. Every day, a theoretical lecture will be followed by a computer practical session (Stata) for attendees to see how to perform statistical analyses and interpret and evaluate the results. All material (including slides, datasets, .do files, and solutions) will be available to attendees prior to the start of the course.
Event Location
ContactFor all queries in regards to this Course please contact the following :- Giorgio Di Gessa or Paola Zaninotto
PLEASE ONLY CONTACT THE ONLINE STORE DIRECTLY IF YOU ARE EXPERIENCING PROBLEMS WITH YOUR DEBIT/CREDIT CARD PAYMENT, FOR ALL OTHER QUERIES RELATING TO THIS COURSE, INCLUDING CANCELLATIONS THESE SHOULD BE DIRECTED TO THE CONTACT DETAILS ABOVE. More InformationThe course would be suitable for full-time and part-time students in any year of study and members of staff. It is aimed at those using quantitative data in any scientific or educational research area. Attendees must have a basic knowledge of regression modelling techniques.
Training will take place online 9:30 to 12.30 on 19-21 March 2025.
Content · Introduction to clustered data; The distinction between levels and variables; The distinction between fixed and random classifications; Random Intercept Models; Random Slope Models; Level 1 and 2 explanatory variables; Cross-level interactions.
By the end of this course you will be able to: · Understand why analysis of clustered data requires methods that take account of this structure of the data · Use and compare different methods for analysing multilevel data · Gain experience in building models for analysing multilevel data · Interpret and communicate results
Organisers: Dr Giorgio Di Gessa and Prof Paola Zaninotto Emails to [email protected] | ||||||||||||||||||||||||
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