This three-day course provides a guide to experimental economics researchers on best practices for translating an idea for an experimental design to an implementable experiment, whether in the laboratory or online. In contrast to courses which focus on software packages (such as oTree or z-Tree courses), on this course you will learn how to create a program that is efficient, reliable, and which enables collaboration, adaptability, and reproducibility.

In the first part of the course, we will implement two sets of commonly seen experiments using oTree. We will introduce a conceptual framework for implementing experimental designs, which includes defining the different states of an experiment, and developing storyboards and wireframes for the information screens, input screens, and calculations/output screens. We will then demonstrate how to translate the designs to software, including topics such as defining groups and matchings, sequential decision-making, multiple decision-making periods, randomization devices, and systematically recording variables for logging and reproducibility.

In the second part of the course, we will provide a brief introduction to key technologies used in producing professional-looking applications, including webpage generation via templates, CSS and Bootstrap, and JavaScript and jQuery. There will also be opportunities for discussions centered on specific aspects of experimental designs based on individual participants’ needs. Throughout, we will emphasize the use of git and GitHub for version control and collaboration, and management of Python virtual environments for reproducibility and portability.

This is therefore not just another oTree course! By the end of the course, you will acquire skills which are transferrable to other platforms or technologies.