Best practices for oTree programming
- Plan your experiment in advance: Before you start coding your experiment, it’s important to plan out the design and protocol of your study. This will help you to identify any potential issues or challenges in advance and ensure that your experiment runs smoothly.
- Test your experiment: Once you’ve written your experiment, be sure to thoroughly test it to identify any bugs or errors. This will help you to catch any issues early on and ensure that your experiment runs as expected.
- Use built-in features: oTree has many built-in features and functions that can help you to create and run experiments more efficiently. Be sure to take advantage of these features, such as randomizing treatments, displaying feedback to participants, and collecting and exporting data.
- Keep your code organized: As your experiment grows in complexity, it’s important to keep your code organized and easy to understand. This will help you to identify issues more easily and make changes more efficiently.
- Use version control: Version control tools like Git can help you to track changes to your code over time and collaborate with other researchers. This can be especially useful if you are working on a team or sharing your code with others.
- Use clear and concise language: When designing your experiment, be sure to use clear and concise language in your instructions and feedback messages. This will help to ensure that participants understand what is expected of them and can complete the experiment successfully.
- Document your code: Finally, be sure to document your code thoroughly, including comments and explanations of any complex functions or logic. This will help you and others to understand the code more easily and make changes more efficiently.
By following these best practices, you can create high-quality experiments using oTree and ensure that your research studies produce reliable and robust results.
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