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Recruiting Software Engineers on Prolific
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Recruiting participants for software engineering research has been a primary concern of the human factors community. This is particularly true for quantitative investigations that require a minimum sample size not to be statistically underpowered. Traditional data collection techniques, such as mailing lists, are highly doubtful due to self-selection biases. The introduction of crowdsourcing platforms allows researchers to select informants with the exact requirements foreseen by the study design, gather data in a concise time frame, compensate their work with fair hourly pay, and most importantly, have a high degree of control over the entire data collection process. This experience report discusses our experience conducting sample studies using Prolific, an academic crowdsourcing platform. Topics discussed are the type of studies, selection processes, and power computation.
Forward citations
Cited by 2 Pith papers
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Exploring Dependence, Overreliance, and Addiction Related Behaviors Associated with Large Language Model Use Among Software Engineers
Software engineers report habitual, functional dependence on LLMs and some overreliance, with addiction-like behavior appearing marginal.
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The Influence of Fraudulent AI-Generated Responses on Software Engineering Surveys
Suspicious or AI-assisted survey answers leave most SE quantitative distributions stable but materially change qualitative framing, code prominence, and interpretive evidence.
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