Introduction to R with applications to Network Analysis
Date:
26/08/2021 - 27/08/2021
Organised by:
Ulster University
Presenter:
Dr Eoin McElroy
Level:
Intermediate (some prior knowledge)
Contact:
Dr Orla McBride
+442870123987
o.mcbride@ulster.ac.uk
Venue: Online
Description:
The first day of the course will introduce attendees to the basic functions of R, assuming no prior programming knowledge or experience. Particular attention will be paid to introducing participants to a wide range of data exploration and visualisation techniques. Attendees will also gain experience of conducting a range of common statistical techniques used in the behavioural and social sciences (e.g. descriptive and inferential statistics, correlational analysis). Comparisons between the functionality of R and other standard data analysis software packages (e.g. SPSS) will be made. Participants will be shown how to install R packages for additional functionality.
Day 2 advances on the first day by focusing on using R to conduct network analysis, a statistical procedure that is used to visualise and study complex relationships between variables. Participants will be introduced to the fundamental concepts in network analysis and will be taught how to use specific R packages (e.g. qgraph) to explore and analyse network data, as well as learning how to visualize networks.
This course uses lectures to provide a clear understanding of the logic underlying the use of statistical techniques and procedures; however, a greater amount of time will be devoted to giving participants experience of hands-on use of R.
Course fees:
Student: £220 (more than one course £198)
Educational/charitable sector: £330 (more than one course £297)
Government/commercial sector: £400 (more than one course £360)
A 10% discount will be given where the booking is for more than one short course.
Attendance: 9:30am-4:30pm daily
Cost:
Student discount available. Multiple course booking available
Website and registration:
Region:
Northern Ireland
Keywords:
Quantitative Data Handling and Data Analysis, Latent class analysis, Latent profile analysis, network analysis
Related publications and presentations:
Quantitative Data Handling and Data Analysis
Latent class analysis
Latent profile analysis