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This is the study programme for 2020/2021.


Learning outcome

Knowledge
Students should be conversant about randomized experiments and have the basic tools to plan and to conduct randomized experiments.
Skills
Students should be able to
  • Understand causal modeling
  • Understand relationship of randomization to causal modeling
  • Gain the statistical tools to analyze randomized experiments
  • Become aware of underlying assumptions, strengths, and weaknesses of experimental approaches
  • Become acquainted with key players in the development and implementation of randomized experiments
  • Gain the statistical tools to design randomized experiments
  • Understand other key issues in design and implementation of random experiments.

General competence
The student should be conversant in the world of causal modeling. They should be able to be consumers and producers of experimental research. They should have the background to develop and implement ethically sound and scientifically valid randomized controlled trials. They should also have proficiency in software designed to measure statistical power.

Contents

This course focuses on the methodology of randomization in social science research. We focus on questions surrounding the use of randomization. Why is randomization so compelling? What assumptions are inherent in randomized designs? What are the hidden challenges to randomization? Is randomization always the "best" empirical strategy? How does one design randomized experiments? Is clustering a problem to randomization?
Additionally the course address important implementation issues in randomized controlled trials in social sciences, such as attrition, fidelity and compliance. What can be done to assure low attrition, and high fidelity and compliance? The course discusses the practical aspects of running a randomized controlled trial including a discussion of its growing importance in policy formation and evaluation.
Finally, the students will be introduced to mixed method designs for investigating why or why not an intervention is effective. The class discusses how mixed methods are necessary for understanding mechanisms, fidelity, and compliance. Particularly, qualitative data collection can inform the researcher about each of these issues prompting improved data collection and rigorous scaffolding of complementary research designs.

Required prerequisite knowledge

Participants must be enrolled in a PhD program.

Recommended previous knowledge

It is mandatory that the students have basic knowledge of quantitative methods at master level.

Exam

Weight Duration Marks Aid
Term paper1/1 Pass - Fail
  • Full participation during the whole course is required.
  • Generally active participation in discussions in general.

In addition, there is a term paper requirement ( 8-10 pages, demonstrating knowledge and application skills).

Course teacher(s)

Course teacher
Simone Häckl
Course coordinator
Eric Perry Bettinger

Method of work

The course will be conducted digitally this year, and will consist of five days of intensive lectures, PC-lab exercises and seminars. During lectures students will engage in the learning objectives and discuss examples of sophisticated RCTs. During the labs students will work with software to measure statistical power. During the seminars, RCT researchers will visit and present large scale RCTs that they are working on, as practical examples.
Students are expected to prepare for and review lecture materials on their own.

Open to

The course is open to interested PhD candidates at the University of Stavanger and other universities. Single Course Admission to PhD-Courses.

Course assessment

Following the regular practice at the UiS.

Literature


Link to reading list


This is the study programme for 2020/2021.

Sist oppdatert: 23.09.2020