Measuring Lineup Difficulty By Matching Distance Metrics With Subject Choices in Crowd-Sourced Data
| Autor: | Mahbubul Majumder, Heike Hofmann, Dianne Cook, Niladri Roy Chowdhury |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Statistics and Probability
Matching (statistics) Relation (database) business.industry Computer science Inference 020207 software engineering 02 engineering and technology computer.software_genre 01 natural sciences 010104 statistics & probability Exploratory data analysis Data visualization Sampling distribution 0202 electrical engineering electronic engineering information engineering Test statistic Discrete Mathematics and Combinatorics Data mining 0101 mathematics Statistics Probability and Uncertainty business Statistical graphics computer |
| DOI: | 10.6084/m9.figshare.5221270 |
| Popis: | Graphics play a crucial role in statistical analysis and data mining. Being able to quantify structure in data that is visible in plots, and how people read the structure from plots is an ongoing challenge. The lineup protocol provides a formal framework for data plots, making inference possible. The data plot is treated like a test statistic, and lineup protocol acts like a comparison with the sampling distribution of the nulls. This article describes metrics for describing structure in data plots and evaluates them in relation to the choices that human readers made during several large Amazon Turk studies using lineups. The metrics that were more specific to the plot types tended to better match subject choices, than generic metrics. The process that we followed to evaluate metrics will be useful for general development of numerically measuring structure in plots, and also in future experiments on lineups for choosing blocks of pictures. Supplementary materials for this article are available online. |
| Databáze: | OpenAIRE |
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