Data-driven Biased Decision-making? - Exploring the landscape between dashboards, visualization literacy and decision bias
(2018) INFM10 20181Department of Informatics
- Abstract
- Data quantities and their sources have amplified over the years and so has the trend to employ dashboard-based data visualizations into the hands of a wider audience of end-users. By selecting four of the most common data visualization formats and combining these into a dashboard this thesis quantitively explored the relationship between similarity features of dashboard-based data visualizations, interpretation accuracy and systematic errors in decision-making i.e. decision biases as defined by Kahneman and Tversky (1974). By sampling 87 business practitioners through a double-blind randomized field experiment conducted at a large IT-company in Sweden, the objective of this thesis was to gauge the nature and extent of the relationship... (More)
- Data quantities and their sources have amplified over the years and so has the trend to employ dashboard-based data visualizations into the hands of a wider audience of end-users. By selecting four of the most common data visualization formats and combining these into a dashboard this thesis quantitively explored the relationship between similarity features of dashboard-based data visualizations, interpretation accuracy and systematic errors in decision-making i.e. decision biases as defined by Kahneman and Tversky (1974). By sampling 87 business practitioners through a double-blind randomized field experiment conducted at a large IT-company in Sweden, the objective of this thesis was to gauge the nature and extent of the relationship between dashboard-based data visualizations, interpretation accuracy and decision biases. The results of the field experiment did not suggest a relationship between similarity features of dashboard-based data visualizations and decision biases. The relationship between peoples’ ability to interpret these data visualizations and decision biases was more nuanced, suggesting no overall bias while a difference between two natural groups with a high and low degree of interpretation accuracy could be demonstrated. The discussion highlights the implications of quantitatively analyzing systematic errors or decision biases that may arise in the expanding territory of dashboard-based data visualizations. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/8950609
- author
- Bergram, Kristoffer LU and Ochan, Brian LU
- supervisor
-
- Odd Steen LU
- organization
- course
- INFM10 20181
- year
- 2018
- type
- H1 - Master's Degree (One Year)
- subject
- keywords
- dashboard, data visualizations, decision bias, visualization literacy, representative heuristic, interpretation accuracy, field experiment, VLAT, base rate neglect, dashboard-based data visualizations, ecological correlation
- report number
- INF18-008
- language
- English
- id
- 8950609
- date added to LUP
- 2018-06-19 13:32:13
- date last changed
- 2018-06-20 14:16:28
@misc{8950609,
abstract = {{Data quantities and their sources have amplified over the years and so has the trend to employ dashboard-based data visualizations into the hands of a wider audience of end-users. By selecting four of the most common data visualization formats and combining these into a dashboard this thesis quantitively explored the relationship between similarity features of dashboard-based data visualizations, interpretation accuracy and systematic errors in decision-making i.e. decision biases as defined by Kahneman and Tversky (1974). By sampling 87 business practitioners through a double-blind randomized field experiment conducted at a large IT-company in Sweden, the objective of this thesis was to gauge the nature and extent of the relationship between dashboard-based data visualizations, interpretation accuracy and decision biases. The results of the field experiment did not suggest a relationship between similarity features of dashboard-based data visualizations and decision biases. The relationship between peoples’ ability to interpret these data visualizations and decision biases was more nuanced, suggesting no overall bias while a difference between two natural groups with a [i]high[/i] and [i]low[/i] degree of interpretation accuracy could be demonstrated. The discussion highlights the implications of quantitatively analyzing systematic errors or decision biases that may arise in the expanding territory of dashboard-based data visualizations.}},
author = {{Bergram, Kristoffer and Ochan, Brian}},
language = {{eng}},
note = {{Student Paper}},
title = {{Data-driven Biased Decision-making? - Exploring the landscape between dashboards, visualization literacy and decision bias}},
year = {{2018}},
}