Apéritif: Scaffolding Preregistrations to Automatically Generate Analysis Code and Methods Descriptions

要旨

The HCI community has been advocating preregistration as a practice to improve the credibility of scientific research. However, it remains unclear how HCI researchers preregister studies and what preregistration users perceive as benefits and challenges. By systematically reviewing the past four CHI proceedings and surveying 11 researchers, we found that only 1.11% of papers presented preregistered studies, though both authors and reviewers of preregistered studies perceive it as beneficial. Our formative studies revealed key challenges ranging from a lack of detail about the study design, hindering comprehensibility, to inconsistencies between preregistrations and published papers. To explore ways for addressing these issues, we developed Apéritif, a research prototype that scaffolds the preregistration process and automatically generates analysis code and a methods description. In an evaluation with 17 HCI researchers, we found that Apéritif reduces the effort of preregistering a study, facilitates researchers' workflows, and promotes consistency between research artifacts.

著者
Yuren Pang
University of Washington, Seattle, Washington, United States
Katharina Reinecke
University of Washington, Seattle, Washington, United States
Rene Just
University of Washington, Seattle, Washington, United States
論文URL

https://dl.acm.org/doi/abs/10.1145/3491102.3517707

動画

会議: CHI 2022

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2022.acm.org/)

セッション: AI: Content Generation

288-289
4 件の発表
2022-05-04 18:00:00
2022-05-04 19:15:00