In this article you will learn how to conduct a distributed data collection with Pro Lab, for screen-based projects.
Distributed data collection enables you to both scale your study and save time. By d
, we mean, the ability to deploy multiple data collection sessions across different locations and eye tracking setups. For example, you can reduce the time needed to reach the sample size goal of your study by running simultaneous data collection sessions with two or more eye tracking setups, or, collaborate with other research teams to perform large- scale and/or geographically separated studies. In this article you will learn how to conduct a distributed data collection with Pro Lab.
In order to see how we implement it in Pro Lab, let’s imagine the following scenario: As a lead researcher you are responsible for running a study where you need to collect data from two subject groups, located in two different geographical locations. In this scenario you can opt to travel to each location with your equipment and perform the data collection yourself, or, instead collaborate with local research groups, and outsource the data collection to their labs.
Let’s go with the second option, as it has the advantage of speeding up your data collection and cuts down on the travelling costs. From previous contacts and collaborations, you know that there are two research groups, one in each location, that have a similar Tobii Pro Eye tracker and a Pro Lab license and have been conducting similar studies, so you decide to contact them and propose to collaborate in this project. You agree the following:
The biggest challenge you have in this scenario, is that outsourcing your data collection requires providing multiple teams access to the experimental materials and procedures needed to complete the same study. This adds higher requirements on transparency and careful documentation of the data collection process. This includes, recruiting and debriefing participants, operating the setup, etc. The following workflow example illustrates how you can take advantage of different features in Pro Lab to help keep the process consistent and transparent across multiple teams.
Below you will find a possible workflow to keep data collection consistency in this scenario:
In this example, you used the the participant management features in pro lab to set your sample size, define the sampling order and standardize the participant information accross sites. Additionaly you used the export features to localise your participants (by selecting only to export part of them to each group).
It is important to instruct the data collection teams do not change the stimulius and trial structure in the Design module of Pro Lab. Any changes won’t be able to later be re-imported into the original project and may create conflicts when aggregating the data.
! The imported project needs to have been generated from the original project.
|Original project||Data collection project||Original project|
|Participant||Participant variable||Value||Change||Import outcome|
|Participant01||Eye color||Set to Green||Changes value to Blue||Conflict, Pro Lab creates a new participant entitled Participant01(2) with Eye color value set to Blue|
|Participant01||Eye color||Not specified||Changes value to Blue||No conflict, the value Blue is assigned to Participant01|
|Participant01||Eye color||Set to Green or Not specified||Adds a new participant variable||No conflict, the new variable and respective value(s) are added to Participant01 in addition to the existing variables|
|Participant01||Eye color||Set to Green||Adds a new value Brown to the existing variable Eye color, and selects it||Conflict, Pro Lab creates a new participant entitled Participant01(2) with the Eye color value set to Brown|
|Participant01||Eye color||Not specified||Adds a new value Brown to the existing variable Eye color, and selects it||No conflict, the value Brown is assigned to Participant01|
|Participant01||Eye color||Set to Green||Deletes the existing variable Eye color||Conflict, Pro Lab creates a new participant entitled Participant01(2)|
|Participant01||Eye color||Not specified||Deletes the existing variable Eye color||No conflict, however Pro Lab discards the change and keeps the variable Eye color for Participant01, with the value set to Not specified|
|Participant01||Eye color||Set to Green||Deletes the value Blue from the variable Eye color||No conflict, the value Green remains assigned to Participant01|
|Participant01||Eye color||Set to Green||Deletes the value Green from the variable Eye Color||Conflict, Pro Lab creates a new participant entitled Participant01(2)|
|Participant01||Eye color||Not specified||Deletes the value Blue or Green from the variable Eye color||No conflict, the variable Eye color value remains Not specified for Partcicipant01|
|Participant01||Eye color||Set to Green or Not specified||Renames Variable Eye color to Iris color||No conflict, a second variable named Iris color is added to Participant01|
|Participant01||Eye color||Set to Green||Renames the value Green to Grey in the variable Eye color||Conflict, Pro Lab creates a new participant entitled Participant01(2) with the Eye color set to the value Grey|
|Participant01||Eye color||Not specified||Renames the value Green to Grey in the variable Eye color||No conflict, the value Grey is added to the variable Eye color as a new value|
Distributed data collection offers different advantages when it comes to saving time or scaling the data collection phase of a study. This in turn allows studies to achieve high statistical power by increasing the sample size and opens the opportunity for different research groups to share and replicate the same study and verify the generalizability of the tested effects.
This article introduces one possible scenario of distributed data collection and how to best implement it with Tobii Pro Lab. The scenario can be used as a start point to implement a distributed data collection procedure in your own study and tweeked to your own needs and type of collaboration.