GESIS Training Courses
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Scientific Coordination

Dr.
Marlene Mauk
Tel: +49 221 47694-579

Administrative Coordination

Angelika Ruf
Tel: +49 221 47694-162

Week 1: Causal Inference and Experiments

Lecturer(s):
Asst. Prof. Dr. D.J. Flynn

Date: 01.03 - 05.03.2021 ics-file

Location: Online via Zoom / Course language: english

About the lecturer - Asst. Prof. Dr. D.J. Flynn

Course description

This course examines lab, survey, and field experimental methods for causal inference. After providing an overview of the essential aspects of experiments, the course focuses primarily on common threats to inference that arise in experimental settings and how to avoid them. Selected topics will include theory testing, treatment design, estimation of heterogeneous treatment effects, convenience sampling and generalizability, preregistration, and ethics. The course will conclude with an applied session in which students design their own experiments, write a pre-registration, and program their experiments in Qualtrics (if applicable). Students will then peer review each other's designs.


Keywords



Target group

Participants will find the course useful if they are interested in:
  • Designing, implementing, and/or analysing experiments that take place in lab, survey, or field settings
  • Becoming more critical consumers of empirical research that purports to demonstrate causality
  • Evaluating the effectiveness of policies or programs on important outcomes in politics, public health, economics, or other fields


Learning objectives

By the end of the course participants will:
  • Design valid lab, survey, and field experiments to test causal hypotheses
  • Be more critical consumers of academic research in a wide range of fields that purports to make causal inferences 
Organisational Structure of the Course:  
Classes will feature a mix of lecture, tutorials, and hands-on exercises. The lectures will have an applied focus: the instructors will highlight key concepts that are necessary to design, implement, and analyse experiments. The hands-on sessions will ask students to design their own experiments from a research area of their choice. Specific hands-on activities will include writing a pre-registration, which includes specifying treatment, hypotheses, logistical choices, and so on; programming a survey experiment in Qualtrics; and peer reviewing other students' designs. Lecturers will be available throughout the applied sessions to answer questions and offer support.


Prerequisites

  • At least one graduate-level statistics course that covers hypothesis testing and regression
  •  
    Software requirements
    Students should bring their own laptops with R or RStudio already downloaded.


    More Information