Creating, designing, and implementing online experiments, particularly in the fields of psychological research and cognitive tasks, involves a careful balance of precision and expense. A popular platform for designing these experiments is Lab.js, but understanding its true cost can be daunting. In this guide, we’ll examine the Lab.js license model, illustrate an example cost of running an experiment with 100 participants, discuss possible hidden costs, and offer tips to reduce expenditure.
Lab.js operates on an open-source license model. This means that researchers can access the platform for free and modify the source code to customize their experiments. However, being open source doesn't necessarily equate to being cost-free. Hidden costs might emerge in the form of labor costs for the setup, programming, and operation stages, as well as in data management and analysis.
An example will provide us with a clearer picture of the costs. A 100-participant experiment designed through Lab.js will predominantly involve labor costs for design, setup, participant recruitment, data collection, and analysis. These costs can vary widely depending on factors such as the complexity of the experiment, the expertise of the team, and the participant pool's compensation, among others.
While Lab.js itself is open-source and available at no cost, there are indirect expenses that can significantly contribute to the cost of running online experiments. These may include wages for trained researchers to conduct and manage the experiment, expenses associated with learning how to use Lab.js effectively, costs to store and backup data, as well as the time and resources necessary to process and analyze the collected data.
Despite these real costs associated with running experiments, some strategies can help reduce the expenses:
Using Lab.js or similar libraries like jsPsych for behavioral experiments and cognitive tasks can indeed cost. However, with effective planning, adequate training, access to supportive communities, and reusable elements, these costs can be managed and minimized while maximizing your experiment's success.