Syllabus
1. Introduction, Working in RStudio (February 21)
Students will be introduced to the content of the course. The R programming environment will be introduced, especially the individual types of information used; commands for saving and uploading data files, their sorting, editing and basic descriptive analysis; work with variables; creation of functions. Students will be introduced to the main advantages of R over other programs, as well as the pitfalls that may occur when working with it.
Reading:
Field, A. P., Miles, J., & Field, Z. (2012). Discovering Statistics Using R. Sage. Chapter 3 – The R Environment.
Gerring, J., & Christenson, D. (2017). Applied Social Science Methodology: An Introductory Guide. Cambridge University Press. Chapter 18 – Univariate Statistics. 2. Data Visualisation (March 6)
Students will be introduced to different ways of visualising data and outputs of quantitative analyses. Ways of data analysis will be revealed through their visualisation. Students will be acquainted with the mistakes that should not be committed in data visualisation. The creation of these visualisations will be practically practised in R.
Reading:
Field, A. P., Miles, J., & Field, Z. (2012). Discovering Statistics Using R. Sage. Chapter 4 – Exploring data with graphs.
Gerring, J., & Christenson, D. (2017). Applied Social Science Methodology: An Introductory Guide. Cambridge University Press. Chapter 19 – Probability Distributions. 3. Statistical Inference and Basic Instruments (March 20)
The logic of statistical inference will be described with an emphasis on meeting the relevant assumptions. Students will get acquainted with the principle of the central limit theorem and acquire the skills of constructing confidence intervals in R. Furthermore, correlation analysis will be presented, especially through situations suitable for its use, the specific form of application and the method of interpretation of results. Students will practically try statistical reasoning and basic analysis in the programming environment R (correlation analysis, t-test).
Reading:
Field, A. P., Miles, J., & Field, Z. (2012). Discovering Statistics Using R. Sage. Chapter 2 – Everything You Ever Wanted to Know About Statistics (Well, Sort of).
Gerring, J., & Christenson, D. (2017). Applied Social Science Methodology: An Introductory Guide. Cambridge University Press. Chapter 20 – Statistical Inference. 4. Linear Regression Analysis (April 3)
The method of regression analysis, which is the leading quantitative method in political science research, will be characterised. Emphasis will be placed on the assumptions of regression analysis, the analysis itself and the interpretation of its results. Students will practically try the application of regression analysis and presentation of results in tabular and graphical form in R. Students will be introduced to the technique of data transformation so that the classic regression analysis can be used.
Reading:
Field, A. P., Miles, J., & Field, Z. (2012). Discovering Statistics Using R. Sage. Chapter 7 – Regression.
Gerring, J., & Christenson, D. (2017). Applied Social Science Methodology: An Introductory Guide. Cambridge University Press. Chapter 22 – Regression. 5. Nonlinear Analysis (April 17)
In some specific cases, it is necessary to proceed to non-linear data analysis. Students will be introduced to logistic regression, especially the application conditions, the analysis itself and the presentation and interpretation of the results. Next, we will focus on the application of negative binomial analysis. The use of all tools will be practised in R.
Reading:
Field, A. P., Miles, J., & Field, Z. (2012). Discovering Statistics Using R. Sage. Chapter 5 – Exploring Assumptions.
Field, A. P., Miles, J., & Field, Z. (2012). Discovering Statistics Using R. Sage. Chapter 8 – Logistic Regression. 6. R Markdown, Mapping (May 15)
Maps are becoming increasingly important in data analysis. Students will therefore get acquainted with the role of spatial analysis in the field of political science research. Practical training will then be carried out using the R Markdown tool, which enables the direct export of analyzes in RStudio to commonly known text documents, PDF files or presentations. Students will learn about the practical use of this tool using the example of creating a regular report for the minister.
Reading:
Lysek, J., Pánek, J., & Lebeda, T. (2020). Who are the voters and where are they? Using spatial statistics to analyse voting patterns in the parliamentary elections of the Czech Republic. Journal of Maps, 17(1), 33–38.
After the seminar:
Xie, Y., Allaire, J. J., & Grolemund, G. (2018). R markdown: The definitive guide. CRC Press. Chapter 2 – Basics.
Annotation
The course acquaints students with advanced tools in the field of data analysis and quantitative political science research, both at the theoretical and especially practical levels. Given that quantitative methods are becoming increasingly important in contemporary political science, their knowledge is a precious and essentially necessary skill.
After completing the seminar, students will be able to work passively with existing research based on quantitative methods and critically evaluate the results of such research. At the same time, they will be sufficiently experienced to actively use the basic statistical tools that are most used in modern political science. In addition, knowledge of the R programming environment will open up other possibilities for students to work with data and, in general, will undoubtedly increase their opportunities for employment.