FSS:MVZb2038 Introduction to Pol. Analysis - Informace o předmětu
MVZb2038 Introduction to Political Analysis
Fakulta sociálních studiíjaro 2024
- Rozsah
- 1/1. 6 kr. Ukončení: zk.
- Vyučující
- Zuzana Ringlerová, Ph.D. (přednášející)
Ing. Mgr. Petr Svatoň (cvičící) - Garance
- Zuzana Ringlerová, Ph.D.
Katedra mezinárodních vztahů a evropských studií – Fakulta sociálních studií
Kontaktní osoba: Olga Cídlová, DiS.
Dodavatelské pracoviště: Katedra mezinárodních vztahů a evropských studií – Fakulta sociálních studií - Rozvrh
- St 10:00–11:40 P31 Posluchárna A. I. Bláhy
- Předpoklady
- ! MVZ238 Introduction to Pol. Analysis && !NOW( MVZ238 Introduction to Pol. Analysis )
None - Omezení zápisu do předmětu
- Předmět je určen pouze studentům mateřských oborů.
- Mateřské obory/plány
- předmět má 47 mateřských oborů, zobrazit
- Cíle předmětu
- This course aims to introduce students to quantitative research design and the teach them how to do basic quantitative analysis and how to meaningfully interpret the analysis. This course gives students the basic background that allows them to use quantitative analysis in their BA or MA thesis, and to further develop their analysis skills in follow-up quantitative courses.
- Výstupy z učení
- By the end of the course, students will be able to do the following:
Understand basic research-design concepts such as variable, hypothesis, causality etc.
Explain how political scientists generate knowledge, including discussion of research designs.
Manage data in a statistical software.
Quantitatively analyze data and interpret the analysis. - Osnova
- Course description
- Why do some democracies fall an other remain stable? What are the causes of war? Why do some people vote and others don't? These are only some of the many important questions studied by political scientists. In this course, students learn about how political scientists study the political world. In addition to discussing theoretical concepts, the course puts a great emphasis on learning practical data-analysis skills. Such skills are valuable in students' professional development as well as in academic work (term papers, bachelor or master theses).
- In the theoretical part of the course, students learn what causal relationships are and what research designs scientists use to establish causal relationships. In the practical part, students acquire basic data-analysis skills such as describing variables in tables and graphs, transforming variables, making comparisons, and performing a multivariate analysis.
- Course outline:
- Week 1: Introduction
- Week 2: Seeing the world as a political scientist: What does it mean?
- Week 3: Establishing causal relationships. How do we know that there is a causal relationship?
- Week 4: Research design. What are the strategies to investigate causal relationships?
- Learning practical skills: Introduction to the software
- Week 5: Measurement. How do we measure concepts of interest?
- Developing analytical skills: Descriptive statistics
- Week 6: Learning practical skills: Transforming variables and labeling variables.
- Week 7: Midterm exam
- Week 8: Learning practical skills: Making comparisons.
- Week 9: Learning practical skills: Making controlled comparisons.
- Week 10: Learning about the population from a sample: Statistical inference.
- Learning practical skills: Comparison of means.
- Week 11: Learning practical skills: Correlation
- Week 12: Linear regression
- Week 13: Learning practical skills: Linear regression.
- Literatura
- James H. Pollock III. 2011. A Stata Companion to Political Analysis Washington DC: CQ Press.
Kellstedt, Paul M. and Guy D. Whitten. 2009. The Fundamentals of Political Science Research. Cambridge: Cambridge University Press.
- Required readings will be available online or in the library.
- James H. Pollock III. 2011. A Stata Companion to Political Analysis Washington DC: CQ Press.
- Výukové metody
- In this course, students will be learning new knowledge and skills in multiple ways:
• Students will learn theoretical concepts from lectures and from the assigned readings.
• Students will learn data-analysis skills in seminars.
• Learning of the data-analysis skills will be reinforced by working on homework assignments. - Metody hodnocení
- Final grade has the following components:
Participation 10%
Homework assignments 40%
Midterm exam 20%
Final exam 30% - Vyučovací jazyk
- Angličtina
- Informace učitele
- If you have any questions about this course, don't hesitate to contact the instructor at ringler@fss.muni.cz
- Další komentáře
- Studijní materiály
Předmět je dovoleno ukončit i mimo zkouškové období.
Předmět je vyučován každý semestr.
- Statistika zápisu (nejnovější)
- Permalink: https://is.muni.cz/predmet/fss/jaro2024/MVZb2038