DOI: 10.5160/psychdata.dhat17ef10
title: Effekt von Clustering Illusion (Cognitive Bias) bei Benutzung einer "Visual Analytics" Umgebung. Forschungsdaten zur Studie 2017
title: Effect of Clustering Illusion during the Interaction with a Visual Analytics Environment. Research data of the 2017 study

description: Clustering Illusion is a cognitive bias and defined as the tendency to see patterns where no patterns exist (Gilovich, 1991; Gilovich, Vallone, & Tversky, 1985). This tendency can be observed when people interpret patterns or trends in random distributions. In the context of the VALCRI (Visual Analytics for Sense-making in CRiminal Intelligence analysis) project eight cognitive biases have been identified which may influence the decision-making process of the analysts. Assessment methods for other cognitive biases exist but this is not the case for the clustering illusion. Based on the study of Cook and Smallman (2007), who studied how cognitive biases affect a JIGSAW "Joint Intelligence Graphical Situation Awareness Web" system, a task that enables to detect the clustering illusion in a visual analytics environment was created. This task was as follows: Participants interacted with a selected set of tools from a visual analytics environment. These tools showed the spatial and chronological distribution of crime incidents in two city districts of Birmingham. In each city district, there were 30 crime incidents. A 2x2 design of random vs. pattern condition and interactive vs static condition was used to detect the influence of patterns and the level of interaction on the decision-making of the participants: 
In the random condition, the crime incidents have been randomly selected from a large set of incidents. In the pattern condition, the incidents have been selected in a way that there are increases or decreases over time and a spatial concentration of incidents in one of the two city districts. In the interactive condition, participants were allowed to interact with the tools to inspect the incidents from different perspectives. In the static condition, participants were asked to inspect the incidents as shown on the screen without interacting with the tools.
After inspecting the incidents for ten minutes, the participants were asked (i) to evaluate if they would increase police presence either in city district A or in city district B, (ii) to evaluate the certainty of their decision, (iii) to announce if their decision was based on the data or patterns and trends in the data and if yes (iv) if they could argue their decision. The univariate analysis of variance showed no significant difference between the random and pattern conditions nor between the interactive and static condition and no interactions. A significant correlation between certainty of the decision and justifying the decision with facts (r=.364, p &lt;.001) was found.
description: Clustering Illusion is a cognitive bias and defined as the tendency to see patterns where no patterns exist (Gilovich, 1991; Gilovich, Vallone, & Tversky, 1985). This tendency can be observed when people interpret patterns or trends in random distributions. In the context of the VALCRI (Visual Analytics for Sense-making in CRiminal Intelligence analysis) project eight cognitive biases have been identified which may influence the decision-making process of the analysts. Assessment methods for other cognitive biases exist but this is not the case for the clustering illusion. Based on the study of Cook and Smallman (2007), who studied how cognitive biases affect a JIGSAW "Joint Intelligence Graphical Situation Awareness Web" system, a task that enables to detect the clustering illusion in a visual analytics environment was created. This task was as follows: Participants interacted with a selected set of tools from a visual analytics environment. These tools showed the spatial and chronological distribution of crime incidents in two city districts of Birmingham. In each city district, there were 30 crime incidents. A 2x2 design of random vs. pattern condition and interactive vs static condition was used to detect the influence of patterns and the level of interaction on the decision-making of the participants: 
In the random condition, the crime incidents have been randomly selected from a large set of incidents. In the pattern condition, the incidents have been selected in a way that there are increases or decreases over time and a spatial concentration of incidents in one of the two city districts. In the interactive condition, participants were allowed to interact with the tools to inspect the incidents from different perspectives. In the static condition, participants were asked to inspect the incidents as shown on the screen without interacting with the tools.
After inspecting the incidents for ten minutes, the participants were asked (i) to evaluate if they would increase police presence either in city district A or in city district B, (ii) to evaluate the certainty of their decision, (iii) to announce if their decision was based on the data or patterns and trends in the data and if yes (iv) if they could argue their decision. The univariate analysis of variance showed no significant difference between the random and pattern conditions nor between the interactive and static condition and no interactions. A significant correlation between certainty of the decision and justifying the decision with facts (r=.364, p &lt;.001) was found.