Schedule, syllabus and examination date

Choose semester

Course content

Political scientists often pose questions of the type “Does X have an effect on Y?” Key to finding an answer is the formulation of a counter-factual: “What would Y have looked like in the absence of X?” The most significant challenge we face when trying to answer such questions with empirical data is that pre-existing conditions may affect both the explanatory factors and the outcomes we are interested in. This course is about ways to overcome this challenge by carefully selecting an appropriate research design and a suitable statistical model. The course aims to provide a toolkit of quantitative techniques for students interested in studying social science with observational data.

The first part of the course centers on experimental and quasi-experimental empirical approaches. We discuss how randomization might reduce concerns about pre-existing conditions and help us estimate causal effects without making many assumptions. We then turn to experimental designs and empirical approaches based on an as if random assumption, such as natural experiments, matching techniques, and regression discontinuity designs.

When randomization is not an option, we often have to make more assumptions to estimate effects. The second part of the course looks at different statistical estimators, with a focus on their advantages, their underlying assumptions and the consequences of breaking them, and the consequences our choice of estimator may have for the conclusions that we can draw. We start with OLS, and then introduce maximum likelihood estimators that in some situations are superior to OLS when studying choice, count, and events data.

Students will get hands-on practice by learning how to implement the various techniques and by replicating existing studies in R. Students will also learn how to present results in a manner that is understandable to a general audience, for example by using graphs and other visual tools. 

Learning outcome


Students will:

  • become familiar with different types of data structures and types of outcome variables, including time-series data, panel data, count data, censored data, and duration data
  • understand the power of randomization and as if random research designs in dealing with confounding factors
  • know the benefits and drawbacks of various statistical models, how to evaluate the validity of the assumptions the models rest on, and in which situations the different models are more and less appropriate
  • know the particular challenges associated with analyzing limited dependent variables, count outcomes, and the duration of events
  • be able to critically read and evaluate existing statistical studies in political science


Students will know how to:

  • evaluate balance and estimate effects in experimental and quasi-experimental studies, using parametric and non-parametric test statistics
  • validate key assumptions and run sensitivity analyses for OLS models
  • estimate, evaluate, and graphically present results from interaction effects
  • address issues of autocorrelation in time series and panel data; for example, through lagging or through conducting simple Granger tests
  • estimate, evaluate and interpret results from discrete choice models, such as logit, ordinal logit, and multinomial logit models
  • estimate count models, such as Poisson and negative binomial models
  • estimate parametric and semi-parametric survival models
  • use simple simulation tools to interpret and present results
  • transform standard (e.g., country-year) data sets into a duration format
  • replicate statistical studies and write their own scripts using R
  • effectively present statistical material in tables and figures


Students have:

  • the ability to critically evaluate empirical research using quantitative observational data
  • a good understanding of the scientific process, including the relation between theory and empirical evidence and between concepts and measures
  • experience analyzing and visualizing a broad range of quantitative observational data using the programming language R


Students who are admitted to study programmes at UiO must each semester register which courses and exams they wish to sign up for in Studentweb.

Students enrolled in other Master's Degree Programmes can, on application, be admitted to the course if this is cleared by their own study programme.

If you are not already enrolled as a student at UiO, please see our information about admission requirements and procedures.

 Apply for guest student status if you are admitted to another Master's programme.


Formal prerequisite knowledge

STV4020A – Forskningsmetode og statistikk or courses with equivalent learning outcomes in OLS regression, logistic regression and a statistical package (such as R).

Overlapping courses

6 credits overlap with STV4025 – Quantitative political science (discontinued)


Lectures and seminars.

Students are required to bring own laptop to lectures and seminars. 


Home exam which includes five subtasks.

  • Each subtask must be between 800-1000 words.
  • The number of lines with R-code cannot exeed 100 in each subtask


Language of examination

You can submit your response in English or Norwegian.

Grading scale

Grades are awarded on a scale from A to F, where A is the best grade and F is a fail. Read more about the grading system.

Explanations and appeals

Ask for an explanation.

Resit an examination

If you are sick or have another valid reason for not attending the regular exam, we offer a postponed exam later in the same semester.

See also our information about resitting an exam.

Withdrawal from an examination

It is possible to take the exam up to 3 times. If you withdraw from the exam after the deadline or during the exam, this will be counted as an examination attempt.

Special examination arrangements

Application form, deadline and requirements for special examination arrangements.


The course is subject to continuous evaluation. At regular intervals we also ask students to participate in a more comprehensive evaluation.

Facts about this course






Autumn 2020


Autumn 2020

Teaching language