MVZ497 Introduction to linear models

Faculty of Social Studies
Spring 2018
Extent and Intensity
1/1/0. 8 credit(s). Type of Completion: zk (examination).
Teacher(s)
Zuzana Ringlerová, Ph.D. (lecturer)
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 13:30–15:00 PC26
Prerequisites
None
Course Enrolment Limitations
The course is only offered to the students of the study fields the course is directly associated with.

The capacity limit for the course is 20 student(s).
Current registration and enrolment status: enrolled: 0/20, only registered: 0/20
fields of study / plans the course is directly associated with
there are 7 fields of study the course is directly associated with, display
Course objectives
Quantitative modeling is an indispensable tool in current political science. Important questions of today, such as: Why do European citizens oppose immigration? Why do some communities recover quickly from an earthquake and others take a long time to recover? Why does corruption persist? are being answered with the use of quantitative modeling. In order to become a critical reader of today's political science, it is important to be familiar with quantitative modeling. The aim of this course is to introduce students to the linear regression model and its variations. Gaining the understanding of the linear regression model provides the first step towards understanding more complex tools in the quantitative toolkit.

The course begins with a brief overview of research design, variable measurement, descriptive statistics, and statistical inference. Then it introduces a bivariate and a multivariate regression model, discusses their assumptions and model interpretation. It then proceeds to discussion of dummy variables and interaction terms. The last part of the course is devoted to dealing with situations when model assumptions are violated: nonlinearity, heteroskedasticity, serial correlation and clustering, and endogeneity.

The course aims to:
Provide students with a deep working understanding of linear regression models and several closely related variations.
Provide students with a strong foundation to enable future learning of other advanced statistical techniques, either in advanced courses, or independently.
Provide students with the skills needed to understand and critique research conducted using quantitative techniques.
Provide students with the skills needed to conceptualize, design, and conduct a rigorous quantitative research project, and write a paper based upon the results.
Provide working knowledge of how to analyze data using a statistical software.
Learning outcomes
By the end of the course, students will be able to do the following:
- Understand of linear regression models and several closely related variations.
- Display a basic understanding of quantitative methods, which will enable future learning of other advanced statistical techniques.
- Demonstrate skills needed to understand and critique research conducted using quantitative techniques.
- Conceptualize, design, and conduct a rigorous quantitative research project, and write a paper based upon the results.
- Analyze data using a statistical software.
Syllabus
  • 1) How do I set up my research to learn whether X causes Y?
  • 2) Getting to know my data
  • 3) Making inferences from a sample to the population
  • 4) Is my X related to Y? Bivariate regression
  • 5) What if I have more than one independent variable? Multiple regression
  • 6) Multiple regression. How do I interpret my model correctly?
  • 7) Including categorical variables
  • 8) Does my independent variable have the same effect under all circumstances? Interaction effects.
  • 9) What if the relationship between X and Y is not linear?
  • 10) Turning to the residuals. Assessing the homoskedasticity assumption and finding remedies.
  • 11) What if my observations are clustered?
  • 12) What to do if we suspect that Y causes X.
  • 13) What to do if the dependent variable is binary.
Literature
  • WOOLDRIDGE, Jeffrey M. Introductory econometrics : a modern approach. Sixth edition. Boston: Cengage Learning, 2016, xxi, 789. ISBN 9781305270107. info
  • KELLSTEDT, Paul M. and Guy D. WHITTEN. The fundamentals of political science research. 2nd ed. Cambridge: Cambridge University Press, 2013, xxiv, 316. ISBN 9781107621664. info
Teaching methods
In this course, you will be learning new knowledge and skills in multiple ways:
You will learn theoretical concepts and practical skills from lectures and from the assigned readings.
You will reinforce the theoretical and practical skills by working on the homework assignments.
You will apply your knowledge and skills in writing your own research paper.
Assessment methods
Course requirements and grading:
Attendance 10%
Homework assignments 35%
Exams 30%
Research project 25%
Language of instruction
English
Further comments (probably available only in Czech)
The course can also be completed outside the examination period.
The course is taught annually.
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