Effect of Clustering Illusion during the Interaction with a Visual Analytics Environment. Research data of the 2017 study

Research Questions/Hypotheses:

The following hypothesis were tested:

Participants will be more sure in their answers and could justify their answer in the pattern condition compared to the random condition.

Participants will be more sure in their answers and could justify their answer in the interactive condition compared to the static condition because they could - in principle - falsify the clustering illusion.

Research Design:

Experimental design; single measurement

Measurement Instruments/Apparatus:

Procedure:

The study has taken place in an examination room of the Graz University of Technology. First, all participants filled out an informed consent form. After that they were introduced to the Visual Analytics for Sense-making in CRiminal Intelligence analysis (VALCRI) platform by a short user manual. The participants had ten minutes time to become familiar with the user interface of the platform. This introduction phase was followed by a general instruction about the tasks. The participants had ten minutes time to complete each task. Participants were reminded by the instructor about the time limitation after five and after nine minutes. After each task, a small break was given. At the end of the fourth task the participants filled out a short demographic questionnaire. The order of the static and interactive condition and the four tasks were balanced. The experiment took one hour time and all the participants got 10 euro expense allowance.

Material and Setting:

For this study the VALCRI (https://www.euprojectvalcri.org/) platform has been used. This platform aims to support the work of the law-enforcement agencies. It was designed to visualise a huge amount of data about criminal incidents.
First, 240 crime incidents in Birmingham´s city districts Coventry and Wolverhampton from the last 6 months (January 2017 to Juli 2017) were randomly selected. After this pre-selection, four examples with 60 crime incidents, 30 per district, were created.

Two of the examples were selected randomly to show no temporal and spatial patterns. The other two examples were created with a noticeable temporal and spatial pattern in one of the two city districts. All of the selected data sets included the following tools: The Location tool, the Time tool and the List tool. The Location tool indicates the spatial distribution of crime incidents, the Time tool shows the temporal distribution of crime incidents and the List tool provides some details about the incidents.
These tools have been selected because they are the most relevant ones for the daily work of analysts.

After each task the participants were asked i) to decide in which district they would increase the police presence, ii) how sure they were in their decision, iii) to announce if their decision was based on the data and if yes, iv) if they could justify their decision.

Data Collection Method:

Data collection in the presence of an experimenter

• Specialized Apparatuses or Measuring Instruments, namely VALCRI Software

Population:

Primarily university students

Survey Time Period:

Sample:

Convenience sample

Gender Distribution:

68,75% female subjects (n=22)
31,25% male subjects (n=10)

Age Distribution: 19-46 years

Spatial Coverage (Country/Region/City): Austria/-/Graz

Subject Recruitment:

The participants were recruited via different social media channels and the intern mail system of University of Graz. The participants recieved 10 Euro expense allowance.

Sample Size:

32 individuals

Return/Drop Out: