Admission requirements
Participation in the seminar is only permitted if the propaedeutic phase has been passed (60 EC).
Description
This course builds upon your knowledge from Statistics 1 & 2, Research Methods in Political Science, Research Design and Data Analysis. Going through this course you will gain fairly in-depth knowledge of R (studying and practicing parts of two online books 'R for Data Science' and 'YaRrr! The Pirate’s Guide to R'). You will discover that R is fantastic not only for making all kinds of graphs, but also for testing 'simple', as well as all kinds of more advanced models (such as Multilevel and Structural Equations). The course follows a ‘hands-on toolbox’ approach (no mathematics involved), where you will be graded on a number of R assignments and your own personal project that will relate to the subject of your bachelor thesis. To give an example of the latter: if you are planning on developing f.i. a most similar case study design (MSCD) for your bachelor project, then you could (using R packages) investigate how a cross cases analysis (cf. John Gerring in the Oxford handbook of Methodology) could help you in selecting the cases for such a design. A rich source of data for such a cross cases analysis is 'Our World in Data'. The last week you will present the results of your findings.
Next to the two online books, we use a number of other online sources (articles, manuals and tutorials), to be mentioned in the syllabus. During this course we will be doing a lot of R code reading. This course is new to the curriculum, so it is possible that there will be some (minor) adaptations along the way. Perhaps a word of warning is in place here: students starting to learning R, sometimes experience a 'steep learning curve', but this should not stop you from getting to know this rich data analysis environment, and we will help you feeling comfortable with this program. If you plan to follow this seminar, make sure you have downloaded and installed R and R Studio on your computer when you come to the first meeting. R is public domain software.
Course objectives
1 Knowledge of R;
2 Knowledge of graphical and modelling data analysis techniques;
3 Gaining insight into the interrelatedness of data models, data analysis techniques and their relative positions within the spectrum of achieving political knowledge.
Mode of instruction
Seminars with slide presentations and R code readings, exercises and homework. Discussion of your homework in the (physical) class room will be done using Kaltura where you will be asked to share your screen.
Assessment method
To complete the final mark, please take notice of the following:
1) The raw final grade for the course is calculated as the (weighted) average of the assignments and the presentation;
2) Coming prepared to the class and participating in class could play a role in rounding the final grade for this course, turning a 6.3 into a seven instead of the (regular) 6.5.
Reading list
Provided in the syllabus available online through Brightspace.
Preparation in January
Study text and exercises (solutions can be found in the sources):
- Chapter 1-10 from YaRrr!
- Chapter 1-5 from R for Data Science.
Timetable
Registration
See general information on tab 'Year 3'.
Contact
Contact information
meijerink@fsw.leidenuniv.nl