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Data Distribution and Mark Shape Affect Visual Trend Estimation in Scatterplots
Date created: 2020-03-31 12:16 PM | Last Updated: 2022-10-10 07:20 PM
Identifier: DOI 10.17605/OSF.IO/HZDF3
Category: Project
Description: A common task with scatterplots is communicating trends in bivariate data. However, the ability of people to visually estimate these trends is under-explored: in cases where visual estimations are systematically different from statistical models, designers may need to intervene to de-bias these estimations, or otherwise inform viewers of the difference between statistical and visual models of trend. We examine the visual estimation of trends, and the potential of mark shape or data distribution to bias these estimates, through a set of laboratory studies and show the results in this project.
This directory contains the estimation results of four experiments, raw data of four experiments, matlab code of data generation, review results of three experiments and supp.pdf.
exp1.csv, exp2.csv and exp3.csv correspond to raw participant data for the three experiments presented in our paper respectively, plus an additional experiment on mark size.
exp1.csv:
Id: id of current participant
Age: a…Files
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