Quantitative Methods I - JPB049

• Anglický název: Quantitative Methods I
• Zajišťuje: Katedra politologie (23-KP)
• Fakulta: Fakulta sociálních věd
• Platnost: od 2025
• Semestr: zimní
• E-Kredity: 5
• Způsob provedení zkoušky: zimní s.:
• Rozsah, examinace: zimní s.:1/1, Zk [HT]
• Počet míst: 10 / 10 (10)
• Minimální obsazenost: neomezen
• 4EU+: ne
• Virtuální mobilita / počet míst pro virtuální mobilitu: ne
• Stav předmětu: vyučován
• Jazyk výuky: angličtina
• Způsob výuky: prezenční
• Další informace: https://docs.google.com/document/d/1jeBV_0bSuraNbo75e_qmmcyoLjJywDlt7IkcPiLJWf4/edit?usp=sharing
• Poznámka: předmět je možno zapsat mimo plán; povolen pro zápis po webu; při zápisu přednost, je-li ve stud. plánu
• Garant: Mgr. Lukáš Hájek, M.A., Ph.D.
• Vyučující: Mgr. Lukáš Hájek, M.A., Ph.D.
• Prerekvizity : {Skupina prerekvizit pro JPB049 a JPB284 (JPB283 nebo JSB535 nebo JPB065 nebo JPB135 nebo JPB150)}

Anotace

The course introduces students to quantitative political science research, especially in a practical sense. Since quantitative methods enjoy substantial importance in political science, knowing them is a valuable and even essential skill. After completing the course, students will not only have passive knowledge for critical work with the outputs of quantitative research, but will also be able to actively use selected statistical analytical tools. Additionally, proficiency in R software will unlock new opportunities for data analysis and significantly enhance students’ competitiveness in the job market.

Poslední úprava: Hájek Lukáš, Mgr., M.A., Ph.D. (10.09.2025)

Cíl předmětu

The course has the following objectives:

• to introduce students to basic and advanced tools of quantitative research,
• to teach students to work with quantitative data and methods using the R software.

Poslední úprava: Hájek Lukáš, Mgr., M.A., Ph.D. (10.09.2025)

Podmínky zakončení předmětu

It is strongly advised to attend all the seminars. Nonetheless, attendance is not mandatory, so students do not have to apologise for an absence. 

The final grade for completing the seminar will be based on meeting the following criteria:

1. Homework (50%) – Seminars 2–5 will be followed by homework assignments, which will recapitulate the procedures learned in the seminar. Groups of 3 students will be created for the joint performance of tasks. Each task will have a share of 12.5% in the final grade.
2. Final paper (50%) – In the same groups, students will prepare a final practical work, which will be assigned in the second half of the semester. The aim of the work will be to test the knowledge of skills learned throughout the semester. The final work will consist of programmed research and a short textual description of this analysis.

Group work is a key part of the course. It supports cooperation, effective task-solving and division of labour, which are key skills in the labour market. In addition, the R language is quite difficult to learn in the initial phase, so in groups, students help each other and teach mutually, thereby verifying their knowledge.

Importantly, late submissions will be penalised by decreasing the initial grade by 4% for each commenced hour of late submission. Every student who starts to fulfil the course’s requirements will be graded at the end of the semester. No retake of any part contributing to the final assessment is possible. 

The course uses the following grading scale of the Faculty of Social Sciences:

• 90.01–100.00% (A – excellent),
• 80.01–90.00% (B – very good),
• 70.01–80.00% (C – good),
• 60.01–70.00% (D – satisfactory),
• 50.01–60.00% (E – sufficient),
• 0.00–50.00% (F – fail).

Poslední úprava: Hájek Lukáš, Mgr., M.A., Ph.D. (17.09.2025)

Literatura

The most important resource for teaching the course is a textbook presenting the teaching of quantitative research methods using the statistical software R:

• Field, A. P., Miles, J., & Field, Z. (2012). Discovering Statistics Using R. Sage.

In case of further interest, the following literature is recommended:

• Gerring, J., & Christenson, D. (2017). Applied Social Science Methodology: An Introductory Guide. Cambridge University Press.
• James, G., Witten, D., Hastie, T., & Tibshirani, R. (2017). An Introduction to Statistical Learning with Applications in R. Springer.
• Meys, J., & De Vries, A. (2012). R For Dummies. Wiley.
• Monogan III, J. E. (2015). Political Analysis Using R. Springer.
• Rumsey, D. (2007). Intermediate Statistics for Dummies. Wiley.

Poslední úprava: Hájek Lukáš, Mgr., M.A., Ph.D. (10.09.2025)

Metody výuky

The course is focused as much as possible on the practical use of quantitative research methods in political science. For this reason, the teaching is conducted in the form of seminars. Seminars are held once every two weeks, in a block form, always lasting 160 minutes of pure time. Only thanks to this teaching format it is possible to first discuss the necessary material theoretically and then immediately present it in a practical form. Thus, the seminars typically consist of an introductory presentation of the given topic, which is followed by practice of applying a specific analytical method with the help of real examples, data and calculations from the field of political science research. As a result, the seminars require the active participation of all students.

Students are obliged to prepare for each seminar by reading the assigned mandatory literature. Knowledge of it is crucial for work during the seminar. The relevant literature will be available in electronic form in the Moodle information system to the maximum extent possible. Students are also expected to complete homework assignments, which serve to practice the taught methods.

Access to literature, seminar materials, assignments, and group communication will be done through the relevant Moodle page.

Work in seminars will be conducted in the R programming environment. Its advantage is free availability and variability of usage options. A well-known disadvantage of the program is its relative difficulty for beginners. However, this is the price for a wide range of usage options. However, this initial difficulty can be overcome quite quickly if the programming environment is studied thoroughly.

R can be installed for free. The easiest way to work with the R programming environment is within the graphical interface for R, which is (for example) RStudio. Both R and RStudio can be downloaded for free. Since we will work with RStudio in seminars, all students must have both R and RStudio installed on their laptops.

Students may use generative AI tools, such as ChatGPT, Copilot, Gemini, Perplexity and similar, provided that the following conditions are met. Specifically, they may be used for the purposes of proofreading, text analysis, translations, searching for sources or generating code (however, students have to make sure that they fully understand the code in each situation). On the contrary, the prohibition of use applies to the text creation of homework and seminar papers. Any use must be cited in the final output in accordance with academic rules. Generative AI tools will not be used to evaluate student outputs. Any violation of these rules will lead to a reduction in grade, failure to complete the course or disciplinary proceedings, depending on its severity. Only compliance with the aforementioned rules will guarantee the fulfilment of the educational objectives of the course, protect equal conditions for all students, and also ensure their privacy. The aforementioned potential sanctions also apply to any plagiarism.

Poslední úprava: Hájek Lukáš, Mgr., M.A., Ph.D. (17.09.2025)

Sylabus

1. Introduction, Working in RStudio (October 10)

Students will be introduced to the content of the course. The R programming environment will be presented, 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. Los Angeles: Sage. Chapter 3 – The R environment.
• Gerring, J., & Christenson, D. (2017). Applied social science methodology: an introductory guide. Cambridge; New York: Cambridge University Press. Chapter 18 – Univariate Statistics.
• Gerring, J., & Christenson, D. (2017). Applied social science methodology: an introductory guide. Cambridge; New York: Cambridge University Press. Chapter 17 – Data Management.

2. Data Visualisation (October 24)

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. The creation of these visualisations will be practised in R.

Reading:

• Gerring, J., & Christenson, D. (2017). Applied social science methodology: an introductory guide. Cambridge; New York: Cambridge University Press. Chapter 19 – Probability Distributions.

3. Data Management (November 7)

Loops and functions will be introduced in R. Moreover, the seminar will focus on working with online data and its presentation using the ggplot2 package. Lastly, students will be acquainted with the mistakes that should not be committed in data visualisation. 

Reading:

• Field, A. P., Miles, J., & Field, Z. (2012). Discovering Statistics using R. Los Angeles: Sage. Chapter 4 – Exploring data with graphs.

4. Statistical Inference (November 21)

The logic of statistical inference will be described with an emphasis on meeting the relevant assumptions. Students will become familiar with the principle of the central limit theorem and acquire skills in constructing confidence intervals in R.

Reading:

• Field, A. P., Miles, J., & Field, Z. (2012). Discovering Statistics using R. Los Angeles: 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; New York: Cambridge University Press. Chapter 20 – Statistical Inference.

5. Basic Statistical Tools (December 5)

Correlation analysis, t-test, ANOVA, and chi-square test will be introduced, especially through situations suitable for their use, specific forms of application and methods of interpretation of results. Students will practically try out statistical reasoning and basic analysis in the R programming environment.

Reading:

• Field, A. P., Miles, J., & Field, Z. (2012). Discovering Statistics using R. Los Angeles: Sage. Chapter 6 – Correlation.
• Gerring, J., & Christenson, D. (2017). Applied social science methodology: an introductory guide. Cambridge; New York: Cambridge University Press. Chapter 21 – Bivariate Statistics.

6. Linear Regression Analysis (December 19)

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. Los Angeles: Sage. Chapter 7 – Regression.
• Gerring, J., & Christenson, D. (2017). Applied social science methodology: an introductory guide. Cambridge; New York: Cambridge University Press. Chapter 22 – Regression.

Poslední úprava: Hájek Lukáš, Mgr., M.A., Ph.D. (10.09.2025)

Vstupní požadavky

There is a formal prerequisite (JPB283, JSB535 or JPB065). Besides this, no special programming knowledge is required to enrol in the course. Nonetheless, the teaching of the course is conducted in an intensive form with the requirement of high commitment from students.

Poslední úprava: Hájek Lukáš, Mgr., M.A., Ph.D. (10.09.2025)