Approachable modeling and smart methods: a new methods field of study

Schimpf, Corey; Castellani, Brian

Abstract

Advances in the integration of smart technology with interdisciplinary methods has created a new genre, approachable modeling and smart methods – AM-Smart for short. AM-Smart platforms address a major challenge for applied and public sector analysts, educators and those trained in traditional methods: accessing the latest advances in interdisciplinary (particularly computational) methods. AM-Smart platforms do so through nine design features. They are (1) bespoke tools that (2) involve a single or small network of interrelated (mostly computational) methods. They also (3) embed distributed expertise, (4) scaffold methods use, (5) provide rapid and formative feedback, (6) leverage visual reasoning, (7) enable productive failure, and (8) promote user-driven inquiry; all while (9) counting as rigorous and reliable tools. Examples include R-shiny programmes, computational modeling and statistical apps, public-sector data management platforms, data visualisation tools, and smart phone apps. Critical reflection on AM-Smart platforms, however, reveals considerable unevenness in these design features, which hamper their effectiveness. A rigorous research agenda is vital. After situating the AM-Smart genre in its historical context and introducing a short list of platforms, we review the above nine features, including a use-case on how AM-Smart platforms ideally work. We end with a research agenda for advancing the AM-Smart genre.

Citation

Schimpf, C., & Castellani, B. (2024). Approachable modeling and smart methods: a new methods field of study. International Journal of Social Research Methodology, 27(1), 1-15. https://doi.org/10.1080/13645579.2022.2111817

• Journal Article Type: Article
• Acceptance Date: Aug 5, 2022
• Online Publication Date: Aug 17, 2022
• Publication Date: 2024
• Deposit Date: Sep 21, 2022
• Publicly Available Date: Sep 22, 2022
• Journal: International Journal of Social Research Methodology
• Print ISSN: 1364-5579
• Electronic ISSN: 1464-5300
• Publisher: Taylor and Francis Group
• Peer Reviewed: Peer Reviewed
• Volume: 27
• Issue: 1
• Pages: 1-15
• DOI: https://doi.org/10.1080/13645579.2022.2111817
• Public URL: https://durham-repository.worktribe.com/output/1190957