28 July 2018
on course website
Data Analysis in R - Session 2
With the increasing use of alternative software packages like R in data analysis, now is the time to learn their ins and outs. The large number of active programmers creating R packages makes this an up-to-date programme providing a huge range of statistical analyses. Researchers also use R to write functions for analysing data, or to create professional plots.
This course focuses upon understanding statistical models and analysing the results whilst learning to work with R. As well as introducing the software to newcomers, it presents basic and more advanced statistics using an overarching framework of generalized linear modelling. We start with descriptive statistics, before moving on to basic tests and simple regression. You also learn how to analyse the multi-item scales which are often applied in survey research using exploratory and confirmatory factor analysis. We then introduce the generalized linear framework to analyse non-normally distributed variables and, lastly, multi-level modelling.
Throughout the course you will work with R, conducting exercises that teach you how to analyse multi-item scales, how to analyse relationships among binary and interval variables and how to apply generalized linear regression models.
By the end of the two weeks you are acquainted with various popular R packages, can write your own functions and can use attractive plots to present your data.
Dr Meike Morren/ Andrea Bassi
Students or professionals in the field of Economics, Social Sciences or any other field with an interest in quantitative data analysis using R. No programming experience is required. PhD students with a deficit in statistics or wishing to refresh their knowledge are also welcome. Our courses are multi-disciplinary and therefore are open to students with a wide variety of backgrounds.
At the end of this course you can:
•Evaluate the quality of quantitative data sources.
•Choose the appropriate method for an analysis, depending upon the data source.
•Conduct various statistical tests.
•Analyse data using generalized linear framework.
•Decide when to use latent variable modelling.
•Enjoy your developed programming skills.
Contact Hours: 45
If you want to earn more credits you can take courses in our other sessions to create a 4 or 6 week programme.
EUR 1000: The tuition fee includes:
• Airport pick-up service
• Welcome goodie bag
• Orientation programme
• Course excursions
• On-site support
• Emergency assistance
• Transcript of records after completion of the course
An early bird discount of €150 is available for students who apply and pay before 15 March, and students from VU Amsterdam as well as from exchange partner universities will receive a €250 discount. You apply for the discount simply by indicating that you are currently a student at VU Amsterdam or at a partner university in the online application.
There are also discounts for students who attend multiple sessions, combine 2 courses and receive a €200 discount and combine 3 to receive a €300 discount. All courses include excursions. We will also organize trips and excursions as part of our social programme, which is a great way to get to know your fellow students and learn more about Amsterdam and the Netherlands. The social programme is not included in the tuition fee.
Furnished accommodation is available. Various housing options will be offered.
The VU Amsterdam Summer School offers ten scholarships that cover the full tuition and housing fees of one course. Information about how to apply for the scholarship will be posted on the VU Amsterdam Summer School website.Register for this course
on course website