RSS01.D12 Randomized Experimental Methods: Survey, Lab, Field and Conjoint Experiments​

This five-day course offers participants an opportunity to design and program their own experiments. It offers an overview of the most popular approaches, namely lab, field, and survey experiments. It is intended for researchers who have a basic understanding of statistics and quantitative methods but have not designed and/or fielded their own experiment yet.

The course will cover theoretical foundations and practicalities of lab and field experiments, placing special emphasis on design choices (external and internal validity) and ethical considerations. The instructor will discuss lab-in-the-field, as well as audit experiments. He will also illustrate some of the typical modelling approaches using data from his own experiments, as well as publicly available replication data.

The course will also cover survey experiments, which have become an essential part of the methodological toolkit of quantitative scholars. Participants will have the opportunity to acquire the skills needed to randomize questionnaire blocks or items in Qualtrics. The course will also provide a brief overview of access panels and online marketplaces (Lucid, MTurk, Prolific, etc.) where the bulk of survey experiments are fielded in contemporary social sciences, critically comparing aspects such as data quality, accessibility, and cost efficiency.

One day (two sessions) will be entirely dedicated to conjoint experiments, which have recently gained popularity in political science and sociology, given their cost efficiency, as well as their ability to approximate real-world decision making, to test multiple competing hypotheses parallel and to limit social desirability bias. The course will also offer some pointers on common approaches to visualize experimental data.

We also cover responsible research conduct during experimental research, considerations of research ethics and best practices for debriefing experimental subjects when deceptive research designs are used. To avoid underreporting of outcome variables and experimental manipulations, as well as other dubious research practices, commonly known as "p-hacking", pre-registration of experimental studies have become the norm. The course will teach you how (and where) to pre-register your future study and present best practices for pre-analysis plans (PAPs).


19 June 2023 - 23 June 2023
Course Fee

Regular: €995
Students & PhD's: €645

Early Bird Regular: €895 (application deadline* April 1st) 
Early Bird Students & PhD's: €580,50 (application deadline* April 1st)

Scholarships and discounts Find more information here
Application deadline

May 1st

*Your application is only completed when the course fee has been paid

Course leader Daniel Kovarek
Level of participant
  • Master
  • PhD
  • PostDoc
  • Professional
Admission requirements ​Some experience with inferential statistics; basic understanding of quantitative research and concepts of causal inference. Ideally, prospective participants have conducted some analyses (e.g., regression analysis, comparing means, etc.) on observational data in the past. Familiarity with statistical software (R, STATA, etc.) for conducting analyses on experimental data is helpful but not a requirement.
Admission documents
  • To get the student/PhD discount you need to upload a copy of your Student card or other proof of registration
  • If you are not a student/PhD, you can upload an empty document under 'Student Card'.
Mode of Study On Campus
ECTS 2 or 4 Find more information here
Location Radboud University