SubjectsSubjects(version: 901)
Course, academic year 2021/2022
  
Researching International Politics: Quantitative Methods - JPM628
Title: Researching International Politics: Quantitative Methods
Czech title: Researching International Politics: Quantitative Methods
Guaranteed by: Department of International Relations (23-KMV)
Faculty: Faculty of Social Sciences
Actual: from 2021
Semester: winter
E-Credits: 6
Examination process: winter s.:
Hours per week, examination: winter s.:1/1 Ex [hours/week]
Capacity: 130 / 130 (130)
Min. number of students: unlimited
Virtual mobility / capacity: no
State of the course: taught
Language: English
Teaching methods: full-time
Additional information: https://dl1.cuni.cz/course/view.php?id=3445
Note: course can be enrolled in outside the study plan
enabled for web enrollment
priority enrollment if the course is part of the study plan
Guarantor: Dr. rer. pol. Michal Parízek, M.Sc., Ph.D.
Teacher(s): Dr. rer. pol. Michal Parízek, M.Sc., Ph.D.
Class: Courses for incoming students
Incompatibility : JPM157
Is incompatible with: JPM303, JPM157
Is interchangeable with: JPM285, JPM303
Annotation
Last update: Dr. rer. pol. Michal Parízek, M.Sc., Ph.D. (30.08.2021)
The purpose of this course is to introduce the students of international relations and security studies to political research methods, and specifically to their quantitative branch. Somewhat less formally, students will learn how to create or collect quantitative political data and how to use them to solve practical and/or theoretical political problems. Quantitative data -- information about political phenomena captured and summarized in numbers -- is available literally on every corner, waiting just to be collected and analyzed. In this class, students get the chance to learn how to do it. Being familiar with quantitative methods enables one to make policy decisions on the basis of a solid analysis of hard(er) empirical evidence, and to conduct systematic inquiry into the nature of international political and security phenomena. Last but not least, knowing quantitative methods enables one not be fooled by others when they try to support their arguments with lousy but seemingly sophisticated (because quantitative) analysis. The class does not assume any prior knowledge of statistics or mathematics, essentially beyond elementary school. It does assume, however, a good deal of motivation on the part of students, as the learning curve may be somewhat steeper for some of the students. The powerful (yet free) statistical package called R will be used in the class, in combination with the interface RStudio. Students are well advised to attend all classes and to keep up with the assigned readings as the material covered is highly cumulative.
Aim of the course
Last update: Dr. rer. pol. Michal Parízek, M.Sc., Ph.D. (30.08.2021)

The specific objectives of the course are:

  • to help students understand and appreciate the most important components of research design
  • to help students understand the key principles of causal inference and the way statistics can help them in it
  • to help students understand and use the key tools of descriptive statistics
  • to help students understand and use basic tools of inferential statistics, including linear regression analysis
  • to help students appreciate the possibilities stats give them for their own future careers (or academic research)
Course completion requirements
Last update: Dr. rer. pol. Michal Parízek, M.Sc., Ph.D. (30.08.2021)

Successful completion of this course requires first and foremost active interest in the subject matter. On the formal level, this means you should:

  • if possible, regularly attend the classes (this is recommended but not formally required)
  • read carefully the required textbook readings and do the quizzes based on these readings (always available on the course Moodle site, altogether 20%)
  • do all the problem sets that help you understand and familiarize yourself with the material covered in each of the classes (always available on the course Moodle site, altogether 20%)
  • take the mid-term test (20% of grade)
  • take the final test or deliver a short report on own data analysis (40% of the grade)    
  • obtain more than 50% of the points from the final review or own data analysis (i.e. more than 20 points out of the 40 points available)
  • actively participate in the discussion fora on Moodle (up to 5% bonification)


The following grading scheme is applied:

  • 100-91: A
  • 90-81: B
  • 80-71: C
  • 70-61: D
  • 60-51: E
  • 50 or less: F (fail)


Please note that as much as 40% of the grade is based on your regular assignments during the semester. This means your final grade will build up over the entire course in a very cumulative manner. Having said that, doing well on the mid-term test and on the final test or own final data analysis is equally important.

At the end of the course, students have a choice between taking a final test or conducting own data analysis and writing a short report on that. In general, taking the final test is likely to require somewhat lower time of preparation. However, I recommend that students who would like to continue using and applying quantitative methods conduct the data analysis. In other words, if students feel that they might want to employ quantitative approaches also in the future - be it for their theses or later on in their professional careers - I highly recommend that they prepare the data analysis rather than 'just' taking the test.

The grading scheme is designed so that everyone who regularly prepares himself/herself for the classes will have no problem passing. The relatively benevolent grading scheme notwithstanding, please note that this course does require continuous work. If one loses track of what is happening in the course, it may be extremely difficult to catch up. So students should understand that continuous work on the assignments and the readings is a necessary condition for the success in this course. %On the other hand, if one is able to do well on the weekly homework, one is more than likely to score very well also on the tests.

Literature
Last update: Dr. rer. pol. Michal Parízek, M.Sc., Ph.D. (13.09.2021)

Core textbok

  • Kenneth J. Meier, Jeffrey L. Brudney, and John Bohte, Applied Statistics for Public and Nonprofit Administration, 8th ed. (Wadsworth, 2010)

Additional readings

  • Alan Bryman, Social Research Methods (Oxford: Oxford University Press, 2012).
  • Andy Field, Jeremy Miles, and Zoë Field, Discovering Statistics Using R (London: Sage, 2012).
  • Gary King, Robert O. Keohane, and Sidney Verba, Designing Social Inquiry: Scientific Inference in Qualitative Research (Princeton: Princeton University Press, 1994).
  • Michael S. Lewis-Beck, Applied Regression: An Introduction (SAGE Publications, Inc, 1980).
  • Rein Taagepera, Making Social Sciences More Scientific: The Need for Predictive Models (Oxford University Press, USA, 2008).
  • John Verzani, Simple R: Using R for Introductory Statistics, 2002.
Teaching methods
Last update: Dr. rer. pol. Michal Parízek, M.Sc., Ph.D. (24.09.2021)

The course consists of weekly lectures/seminars. Attendance is highly recommended, although strictly speaking this is not a formal requirement.

Course online sessions take place at https://cesnet.zoom.us/j/92726480217 (Meeting ID: 927 2648 0217).

Syllabus
Last update: Dr. rer. pol. Michal Parízek, M.Sc., Ph.D. (30.08.2021)
  1. Introduction and motivation; (quantitative) research as seeking answers to the right questions
  2. Research design, inference, and causality
  3. Data, data, data: measurement theory, measuring things, levels of measurement
  4. Data, data, data in practice
  5. Key descriptive statistics: measures of central tendency and measures of dispersion
  6. Probability; standard normal distribution, binomial distribution
  7. Statistical inference and hypothesis testing
  8. T-test (testing the difference between two groups) and experiments
  9. Categorical and ordinal variables analysis: Cross-tabs and chi-square; measures of association
  10. Bivariate regression, principles, assumptions, and fit
  11. Multiple regression
  12. Model specification, interactions, and what's next
 
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