Everyday environments as cues to smoke: Personalized environments in virtual reality to elicit smoking cravings - Data and Analysis Code

doi: 10.4121/19433867.v1
The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/19433867
Datacite citation style:
Antoniades, Alkis; Albers, Nele; Brinkman, Willem-Paul (2022): Everyday environments as cues to smoke: Personalized environments in virtual reality to elicit smoking cravings - Data and Analysis Code. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/19433867.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Dataset
Herein is the dataset collected during our VR experiment and the data analysis code used, as part of my MSc thesis project, titled "Everyday environments as cues to smoke: Personalized environments in virtual reality to elicit smoking cravings."

Our experiment involved presenting personalized and non-personalized virtual reality environments to participants and asking them how familiar was the experience at the end of each timed environment presentation. After all environments had been presented, participants were asked to fill-in questionnaires regarding the sense of presence elicited by our system, as well as give scores for the usability of the user interface they were asked to interact with.

We performed data analysis to determine:
1. Whether personalized virtual environments elicited higher experience familiarity than non-personalized ones.
2. Whether sense of presence positively affected elicited experience familiarity.
3. How our system scored on the sense of presence elicited and how it compared to norm values reported by the questionnaire providers.
4. How our system scored on a component-based usability questionnaire and whether it scored higher than a norm value reported by the questionnaire providers.
5. How our system scored on additional usability questions we formulated.

More information can be found in the README file contained.
history
  • 2022-03-31 first online, published, posted
publisher
4TU.ResearchData
format
.zip; .csv; .r; .rmd; .ipynb; .py;
funding
  • Part of Perfect Fit, funded by NWO, project number 628.011.211
organizations
TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, Department of Intelligent Systems, Interactive Intelligence.

DATA

files (1)