The core element in the study setup management is ease-of-use. You can import all kind of stimuli to the study set up: Pictures, videos, instructions, questionnaires, launch websites, etc… You can make as complex study setups as desired with maximun control & flexibility.

Full Stimuli and Respondent Control

Control the test down to the smallest detail without loosing the overview, by using the advanced stimuli sequence and respondent management features. Test on several media types without worrying about stability and robustness of the study.

> Instruction/dummy stimuli support

> Use both dynamic and static stimuli in the same study

> Fixed or random position configuration for each stimulus

> Automatic and manual slide change

Advanced Segmentation

Isolate and analyze subsets of your data with the fast and interactive segment builder. Use the build-in test plan study to segment on as many variables as desired. The segments automatically updates as you add more respondents to your study.

> Fast & intuitive segmentation on common critera such as gender, age, etc…

 > Option to manuallyinclude or exclude single or set of respondents

> Create any number of segmentationsfrom a set of collected data

> Use Excelto build custom test plans for advanced segmentations

Use Excel to make a test plan to setup your study. It enables you full control of your respondent criteria, stimuli and test execution. Stimuli sequencing can be setup uniquely for each respondent – or group of respondents.

• Use Excel to build your test plan – tutorial template is included with the application
• Assign unique stimuli order for respondents or any custom segmentation attributes
• Setup your data collection distribution plan for study sharing
• Re-use test plans on several Attention Tool installations

Use test plans to implement blockdesigns

If you wish to execute a test on a batch of images, where the images can be subcategorised according to some prominent feature, you may consider to implement a block design to control exposure of the images, thus each category is represented by a block.

Example: Test 10 images of cars

5 images feature the cars seen from the front
5 other images feature the cars seen from the left side

One could implement a complete randomisation of all images, however this can create unwanted mixing effects, changing randomly from front to side view. Therefore one might consider to make two blocks:

A: Cars from front, image A1-A5
B: Cars from side, image B1-B5

In most cases you will rotate images within the block, and also rotate the blocks respectively.

This can be implemented with a test plan:
Respondent 1: A1,A3,A5,A4,A2,B5,B1,B3,B2,B4
Respondent 2: B4,B1,B3,B2,B5,A1,A2,A5,A4,A3
Respondent 3: A1,A2,A5,A4,A3,B5,B4,B3,B2,B1
etc…

Furthermore with a block design, you can more easily make statistics on the block level, ie. pool metrics across all A-images, and across B-images.

Attention Tool’s quality management assures to get out the best data without loosing important findings.

Calibration Control

With the build in Eye Finder and calibration validation it is easy to conduct a professional eye tracking study minutes after the introduction to the software. Attention Tool evaluates the calibration quality in real-time and guide the operator to take the necessary action to get the best calibration.

> Live feedback when calibrating your respondent directly monitored from the operators workspace

> Calibration results are quantified into one result parameter – no complex interpretation needed

> Seamless experience for the respondent through a reliable and repeatable process for the data collector

 Data Quality Feedback

Advanced data collection statistics in order to support the moderator during data collection by getting immediate feedback on data quality via validity feedback from the eye tracking platform.

> Feedback available at study level during and directly after respondent has been tested – no analysis needed

> Average data quality of all exposures providing overall respondent performance

> Filter according to a data quality threshold for a complete overview of what stimulus gaze data was sufficient or not

Outlier Detection

> Positioning a respondent to the data quality summary all integrated within the four step workflow

> Interslides to calibrate light reflexes to establish an emotional baseline

> Data benchmarking across studies enabled through the standardized process