MVZb2038 Introduction to Political Analysis

Faculty of Social Studies
Spring 2024
Extent and Intensity
1/1. 6 credit(s). Type of Completion: zk (examination).
Teacher(s)
Zuzana Ringlerová, Ph.D. (lecturer)
Ing. Mgr. Petr Svatoň (seminar tutor)
Guaranteed by
Zuzana Ringlerová, Ph.D.
Department of International Relations and European Studies – Faculty of Social Studies
Contact Person: Olga Cídlová, DiS.
Supplier department: Department of International Relations and European Studies – Faculty of Social Studies
Timetable
Wed 10:00–11:40 P31 Posluchárna A. I. Bláhy
Prerequisites
! MVZ238 Introduction to Pol. Analysis && !NOW( MVZ238 Introduction to Pol. Analysis )
None
Course Enrolment Limitations
The course is only offered to the students of the study fields the course is directly associated with.
fields of study / plans the course is directly associated with
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Course objectives
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.
Learning outcomes
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.
Syllabus
  • 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.
Literature
  • 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.
Teaching methods
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.
Assessment methods
Final grade has the following components:
Participation 10%
Homework assignments 40%
Midterm exam 20%
Final exam 30%
Language of instruction
English
Further comments (probably available only in Czech)
Study Materials
The course can also be completed outside the examination period.
The course is taught each semester.
Teacher's information
If you have any questions about this course, don't hesitate to contact the instructor at ringler@fss.muni.cz
The course is also listed under the following terms Spring 2021, Autumn 2021, Spring 2022, Spring 2023, Spring 2025.
  • Enrolment Statistics (recent)
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