Some Prior(s) Experience Necessary: Templates for Getting Started With Bayesian Analysis
ACM Human Factors in Computing Systems (CHI) 2019
Work of of the code templates we created to help HCI researchers conduct a Bayesian statistical analysis. Sections that require user input are bolded.
Abstract
Bayesian statistical analysis has gained attention in recent years, including in HCI. The Bayesian approach has several advantages over traditional statistics, including producing results with more intuitive interpretations. Despite growing interest, few papers in CHI use Bayesian analysis. Existing tools to learn Bayesian statistics require signi cant time in- vestment, making it di cult to casually explore Bayesian methods. Here, we present a tool that lowers the barrier to exploration: a set of R code templates that guide Bayesian novices through their rst analysis. The templates are tailored to CHI, supporting analyses found to be most common in recent CHI papers. In a user study, we found that the tem- plates were easy to understand and use. However, we found that participants without a statistical background were not con dent in their use. Together our contributions provide a concise analysis tool and empirical results for understanding and addressing barriers to using Bayesian analysis in HCI.
Citation
BibTeX
@inproceedings{some-prior-experience-chi-2019,
title = {Some Prior(s) Experience Necessary: Templates for Getting Started With Bayesian Analysis},
author = {Phelan, Chanda and Hullman, Jessica and Kay, Matthew and Resnick, Paul},
year = 2019,
booktitle = {Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems},
publisher = {ACM},
series = {CHI '19},
doi = {10.1145/3290605.3300709}
}