|
|
|
||
|
This course introduces students of international relations and security studies to political research methods and specifically to their quantitative branch. Given the key role data analysis enjoys in political research and practice, and given how prominently data-analytical skills are demanded in jobs in politics and beyond, the course serves an important role in the development of students’ competences. Students will learn how to create or collect quantitative political data and how to use them to solve practical and theoretical political problems. The classes cover a range of topics from research design principles to data collection and visualization, probability and inference, descriptive statistics, and a series of inferential statistical techniques. Each class discusses the core tenets of the issues covered, but it also introduces more advanced material and insights from the practice of applied political research. Some specialty topics in most recent quantitative methods advances, such as natural language processing, are also touched upon. The class does not assume any prior knowledge of statistics but it does assume a good deal of motivation on the part of students, as the learning curve may be somewhat steeper for some. The powerful free statistical package R will be used in the class, in combination with RStudio. Students thus also acquire practical transferable coding (programming) skills. Poslední úprava: Parízek Michal, doc., Ph.D. (15.09.2024)
|
|
||
|
The specific objectives of the course are:
Poslední úprava: Parízek Michal, doc., Ph.D. (15.09.2024)
|
|
||
|
Successful completion of this course requires first and foremost active interest in the subject matter. On the formal level, this means you need to:
I recommend that students attend all classes, though this is not formally required. Students are also well advised to keep up with the assigned readings as the material covered is highly cumulative.
The mid-term review takes place after class 8. It takes takes place online only, on Moodle (under class 8). You yourselves select a window of up to 3 hours in the time window between Tuesday Nov 26 morning and Friday Nov 29 evening. Within that window, you take the test and submit. The text contains both theoretical classes- and readings-based questions, and calculated questions. Poslední úprava: Parízek Michal, doc., Ph.D. (13.11.2024)
|
|
||
|
Core textbook:
Useful alternative textbooks, providing slightly different frameworks and explanations:
Additional readings:
Poslední úprava: Parízek Michal, doc., Ph.D. (02.09.2024)
|
|
||
|
The course consists of weekly lectures. Attendance is highly recommended, although strictly speaking this is not a formal requirement. From week 3, a significant element of individual work on data analysis is present, whereby students perform at home, on their computers, statistical analysis in MS Excel and in R. This is time-consuming and, for many, a demanding part of the course, but ultimately this individual work is a key part of the learning process. In addition to the weekly classes, across the semester there will be two tutorial sessions with Tereza Plistilova, mostly for troubleshooting purposes. These are meant especially for those who might struggle with some technical aspect of the course or with specific parts of the material covered. The use of AI-powered tools, including generative AI, is permitted in the course for all programming- and analysis-related tasks. However, note that even powerful generative AI models are language models only, as we will learn, there is no guarantee that what they "say" has a close connection to reality. Note that the use of generative AI tools is not permitted for the non-computational, verbal parts of the mid-term and final tests (the open questions there oriented at theory). Poslední úprava: Parízek Michal, doc., Ph.D. (24.09.2024)
|
|
||
|
Exam structure and rules The exam consists of two parts, 1) an online exam taken from home, focused on data and calculations, and 2) an in-person exam, taken in the PC lab in Jinonice, focused on theory (based primarily on lectures and readings). The general structure is similar to that of the mid-term review. 1) The online part consists only of work with data – in R and Excel. It is a fully open-book, open-internet part. 2) The in-person part asks you to answer some closed questions as well as to provide written answers to open questions, typically asking you to explain, in your words a specific concept of method. It is thus based on your understanding of your theory, working primarily with the assigned readings and the material covered in the lectures. The exam format enables you to work with your own hand-written or printed notes or books, or with digital textual/visual materials you will bring with you e.g. on a flash drive (USB stick). By these, I mean in particular pdf files with relevant textual material from the semester. You are advised to prepare such materials in advance. If you want to use such materials, indeed make sure to bring them on a flash drive, you will not be able to access any online materials (e.g. your Google Drive) on the day of the exam in the PC lab. In the in-person part, you are not allowed to use any online sources and are not allowed to use any computer applications. Specifically, you are not allowed to use any AI tools, including and especially generative AI tools. The target of the exam is to evaluate your substantive knowledge of the material covered and, relatedly, your ability to extract specific detailed information from your class materials, if needed. The purpose is not to test your ability to work with generative AI and formulate pertinent prompts. The use of any online materials will be prevented technically by disallowing other applications than Moodle on the computers in the lab. The fairly strict time limit (40 minutes for the in-person part) does not enable you to search for answers unless you are robustly knowledgeable of the matter prior to the exam itself. Please bear this in mind. Please note that all parts of the exam are strictly individual and no cooperation of the student with other students, or anyone else, is allowed. Also, please note that plagiarism is taken very seriously at our Department and Faculty. Any cheating during the exam, including plagiarism, will result in a penalty being imposed (concrete disciplinary measure will reflect the severity of the offence).
Logistics You need to take both parts in one week (in one ‘slot’).In SIS, you always register only for the in-person part - that means, you only have one registration in SIS for your exam slot, although in practice the exam has two parts. Please note the sequence (calculations, theory) may vary from week to week. Also note that for the in-person time, you are always registered for one exact 60-minutes slot, and you need to take the exam in person in that exact slot. Every student needs to verify individually that he or she can reliably log into the university system (using their standard CAS credentials) and computers in the PC lab and, there, log into their Moodle account and this specific course.
Here are the exam times for the academic year 2024/2025. The respective “activities” will appear in Moodle in the “Final exam” section. If you do not see "your slot" in Moodle now, do not worry. They will appear there shortly before the exam itself. Please always verify which exact slot you have registered for for the in-person part.
Exam slot 1: Online part: 13/1, 11:00-12:30 In-person part: individual slots on 16/1, at 8:00, 9:00 and 10:00
Exam slot 2: Online part: 20/1, 11:00-12:30 In-person part: individual slots on 21/1, at 8:00, 9:00 and 10:00
Exam slot 3 (note the reverse order of the two parts): In-person part: individual slots on 27/1, at 11:00, 12:00 and 13:00 Online part: 28/1, 11:00-12:30
Exam slot 4: Online part: 10/2, 11:00-12:30 In-person part: individual slots on 11/2, at 8:00, 9:00 and 10:00 Poslední úprava: Parízek Michal, doc., Ph.D. (16.01.2025)
|
|
||
The troubleshooting tutorials will be scheduled later. Poslední úprava: Parízek Michal, doc., Ph.D. (29.09.2024)
|