GESIS Training Courses
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Scientific Coordination

Sebastian E. Wenz
Tel: +49 221 47694-159

Administrative Coordination

Loretta Langendörfer M.A.
Tel: +49 221 47694-143

Field Experiments

About
Location:
Cologne / Unter Sachsenhausen 6-8
 
General Topics:
Course Level:
Format:
Software used:
Duration:
Language:
Fees:
Students: 550 €
Academics: 825 €
Commercial: 1650 €
 
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Lecturer(s): Johanna Gereke, Nicole Schwitter

About the lecturer - Johanna Gereke

About the lecturer - Nicole Schwitter

Course description

Field experiments are essential tools for testing causal relationships in real-world contexts. This course aims to enhance participants' research skills in the experimental social sciences by providing the conceptual and analytical tools needed to design and implement field experiments that address specific research questions. By the end of the course, participants will be familiar with various types of field experiments, including natural field experiments with confederates, audit and correspondence studies, as well as digital field experiments. Participants will also have developed a credible research design for their own field experiment.
 
The course comprehensively covers each stage of conducting a field experiment, from design and randomization to data analysis, while emphasizing important topics such as subgroup analysis, non-compliance, and research ethics. The format balances theoretical lectures with practical sessions, allowing participants to grasp the principles of field experiments and apply them in their own research.
 
For additional details on the course and a day-to-day schedule, please download the full-length syllabus.
 
Organizational Structure of the Course
Each day of the course will begin with four hours of classroom instruction in the morning, followed by two hours of hands-on tutorials, exercises, and individual meetings in the afternoon. The morning lectures will cover essential methods and general considerations for different types of field experiments while discussing empirical examples. Students are strongly encouraged to engage actively in these sessions by reading the assigned empirical studies, asking questions, and contributing to discussions.
 
In the afternoon, students will work on (reading, designing, and/or coding) assignments individually or in small groups, applying the methods presented in the morning using statistical software like Stata or R. During these sessions, instructors will be available to assist with any questions or to discuss specific challenges. Additionally, two afternoons will be dedicated to individual consultations, allowing students to explore their ideas for field experimental designs. These afternoon sessions will also provide opportunities for students to address their own research problems in one-on-one consultations with the lecturers.


Target group

This seminar-based course is best suited for students who possess a foundational understanding of statistics and basic skills in statistical software such as Stata or R. Participants will engage in reading, discussing, and replicating existing experimental studies.
 
You will find the course useful if:
  • you are interested in applying experimental methods to address causal questions in social science research.
  • you are an advanced master student, PhD student, postdoctoral researcher in quantitative social sciences.
  • you aim to develop practical skills for analyzing experimental data, including handling challenges like non-compliance and attrition.
  • you are concerned about the ethical implications of experimental research and want to learn best practices in research ethics.
  • you plan to conduct a field experiment in your own projects.


  • Learning objectives

    By the end of the course, you will:
  • have gained an overview of the most common field experimental methods;
  • know the strengths and weaknesses of the different field experimental methods;
  • have gained a good understanding of how to design and implement field experiments as well as how to analyze the data and report on the results;
  • can apply the methods purposefully in the context of your own work;
  • understand the practical and ethical limitations and good research practices (e.g. pre-registration)


  • Prerequisites

  • a good basic knowledge of statistics
  • Practical experience in data analysis with a common statistical software (Stata or R).
  •  
    Software and Hardware Requirements
    As a participant, you should bring your own laptop for use in the course. You will be able to use either R or Stata for the coding exercises. The latest version of R is available for free at https://cran.r-project.org/, and RStudio is available at https://www.rstudio.com/. Stata short-term licenses can be provided by GESIS for the duration of the course if needed. Throughout the course, participants will need to be able to install additional user packages on the fly (this requires an internet connection and appropriate writing rights on the local system).


    Recommended readings