2nd Semester 2019/20: Behavioral experiments and open science workshop

Instructors

Patricia Mirabile

If you are interested in this project, please contact Patricia Mirabile by email.

Registration until 31 May 2020

ECTS

6

Description

Behavioral studies can be used to investigate the empirical adequacy of philosophical theories about reasoning, language and logic and there is a growing expectation in behavioral research to follow principles of open and reproducible science.

In this workshop, you will have the opportunity to learn how to empirically test theoretical claims by planning and implementing online experiments and by analysing and discussing data. We will also discuss the challenges presented by experimental research and you will learn about open science and the practices that promote it. 

Organisation

Students--or pairs of students--should come with an idea of a theoretical claim they would like to investigate using empirical methods, and be familiar with some of the relevant theoretical literature.

In the first part of the workshop, students will design and implement an online experiment and draft a registered report describing the intended experimental plan. In the second part, students will conduct statistical analyses on a simulated dataset (provided by me) and propose a discussion of the results. They will also finish preparing the registered report. Interested students will be able to submit their registered report for peer-review to a scientific journal at the end of the project. 

Note: As I will not be able to provide the necessary funding to run the online experiments, we will not be collecting actual data. However, after the conclusion of the workshop, students should feel free to approach relevant ILLC members to propose a collaboration on the research idea. 

Prerequisites

 

Requested: interest in experimental methods and open science, working knowledge of statistics (e.g. linear models, regression coefficients, etc). 

Useful:  experience in collecting experimental data, programming skills in R, Python or/and js.

Assessment

Assessment will be based on active participation in all stages of the project on a pass/fail basis.