The Virtualitics documentation online is not good, especially with regards to what data type is needed in the dataset for each feature and plot type to work. For example, having data on countries in the world does not mean that the 3D map will work. From the videos I saw online and my failed trial and error attempts, it seems to be that longitude and latitude is needed for that.
Additionally, it can be challenging to manipulate the dataset once it is uploaded into Virtualitics, which can pose a challenge for satisfying the different data type criteria for the plot types. For example, a histogram can take a categorical variable and show the frequency by the height of the bars, but for a line plot to show the count of a filtered categorized variable, proved extremely difficult and seemed more time consuming. You needed a more advanced understanding of how to make additional columns of data in Virtualitics, how to change variables to numerical representations, applying sums to those, and inputting that number as a datapoint. Virtualitics does not have detailed documentation on how to complete this beyond identifying data manipulation as a feature.
Virtualitics smart mapping feature was extremely helpful for an initial and speedy orientation of the best visualization approach for the data and then it's much faster to customize from an already completed format. It’s like looking at an example and working backwards, which is helpful in a trial and error system where many of the features are not compatible with the dataset. You get to start with what works in Virtualitics and sub out what does not work with your goals, and you ultimately end up changing less features than if you started from scratch. It also helps brainstorm alternative perspectives to look at the data.
People initially look at the axis instead of the legend to identify the bars. This can often results in misidentification. Athletics was confused for arts competition. Additionally, people commented that it was hard to read the axis with so many categories, so having the bars color-coded was very helpful (which was the case when filtered to only look at female participation (Q3 and Q4)). Another helpful approach to address the difficult-to-read axis is to have someone sharing what the colors and axis are while a partner scales and rotates the graph in 3D. This is great because it makes the Virtualitics experience (even when limited by the number of licenses) even more collaborative.
If there's a setup for a study administrator and participant, then the most efficient approach is for the study to have the participant keep the headset on throughout the entire analysis session. The study administrator will switch between projects as they follow the folder sequence and enter/exit VR mode. This will result in the message "Please Take Off the VR Headset". The participant should ignore this message and not take off the headset.