Year: 2025
Type: article
Abstract: Across disciplines, research often relies on groups of people to participate in experiments or attend events at the same time. Typically, researchers try to maximize attendance by manually identifying a set of times that suit the diaries of many individuals. However, this is inefficient, is prone to error, and can lead to a final sample that is not large enough to provide meaningful inferences. While current scheduling tools are useful for individual-based research, enabling participants to select times convenient to them within a researcher's preset parameters, they are less useful in research that requires specific or flexible group sizes. In response, we present Optimeet, a web application that allows researchers to upload participants' availability data and generate an optimal allocation schedule for multiple groups. We describe the function of the underlying applet, which identifies a schedule to maximize attendance by treating it as a computational problem involving combinatorial optimization (Experiment 1). Our solution relies on an empirical comparison of parameter-free heuristics to make allocation decisions that make the best use of participants' availabilities and the derivation of appropriate performance metrics. Of the algorithms evaluated, one consistently outperformed comparable versions of existing tools, which we verified in a further exercise (Experiment 2) involving a large human sample (N = 5,289). We consider the methodological utility and practical value of these developments, and include detailed documentation, code, and a video tutorial so that researchers can rapidly employ Optimeet to support group research.
Source: Behavior Research Methods
Authors: Lukas W. Mayer, Desislava Bocheva, Joanne Hinds, Olivia Brown, Lukasz Piwek, David A. Ellis
Institutions: University of California, Irvine, University of Bath
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $3,990
OpenAlex ID: W4414275837