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Recruiting Software Engineers on Prolific

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arxiv 2203.14695 v1 pith:VTATVPGQ submitted 2022-03-28 cs.SE

classification cs.SE
keywords datacollectioncrowdsourcingexperienceprolificrecruitingsamplesoftware
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Exploring Dependence, Overreliance, and Addiction Related Behaviors Associated with Large Language Model Use Among Software Engineers

    cs.SE 2026-08 conditional novelty 5.0 of 10

    Software engineers report habitual, functional dependence on LLMs and some overreliance, with addiction-like behavior appearing marginal.

  2. The Influence of Fraudulent AI-Generated Responses on Software Engineering Surveys

    cs.SE 2026-07 conditional novelty 5.0 of 10

    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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