Affiliation:
1. University of Washington, Seattle, WA
2. Eastlake High School, Seattle, WA
Abstract
Data analysis requires translating higher level questions and hypotheses into computable statistical models. We present a mixed-methods study aimed at identifying the steps, considerations, and challenges involved in operationalizing hypotheses into statistical models, a process we refer to as
hypothesis formalization
. In a formative content analysis of 50 research papers, we find that researchers highlight decomposing a hypothesis into sub-hypotheses, selecting proxy variables, and formulating statistical models based on data collection design as key steps. In a lab study, we find that analysts fixated on implementation and shaped their analyses to fit familiar approaches, even if sub-optimal. In an analysis of software tools, we find that tools provide inconsistent, low-level abstractions that may limit the statistical models analysts use to formalize hypotheses. Based on these observations, we characterize hypothesis formalization as a dual-search process balancing conceptual and statistical considerations constrained by data and computation and discuss implications for future tools.
Publisher
Association for Computing Machinery (ACM)
Subject
Human-Computer Interaction
Reference87 articles.
1. Futzing and Moseying: Interviews with Professional Data Analysts on Exploration Practices
2. Fitting linear mixed-effects models using lme4;Bates Douglas;arXiv:1406.5823,2014
3. Package ‘lme4‘;Bates Douglas;CRAN,2019
4. Characterizing Exploratory Visual Analysis: A Literature Review and Evaluation of Analytic Provenance in Tableau
5. Jacques Bertin. 2011. Graphics and Graphic Information Processing. Walter de Gruyter.
Cited by
5 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献