Virtual Reality Experiences

Description

This dataset captures user experiences with virtual reality across demographics, device types, and session characteristics. It includes self-reported immersion and motion-sickness measures, along with basic context like age, gender, headset model, and session duration. Useful for analyzing factors that influence comfort and engagement in VR.

Source: Kaggle — Virtual Reality Experiences by aakashjoshi123
Link: https://www.kaggle.com/datasets/aakashjoshi123/virtual-reality-experiences

Attributes and Types

• UserID — categorical (nominal). Unique identifier for each participant/session.
• Age — quantitative (ratio). Participant age in years.
• Gender — categorical (nominal). Reported gender of the participant.
• VRHeadset — categorical (nominal). Headset used (e.g., Oculus, HTC Vive, etc.).
• Duration — quantitative (ratio). Session duration (time units as provided in the dataset, often minutes).
• MotionSickness — ordered categorical / quantitative discrete. Integer severity scale (e.g., 0 = none to higher values = more severe).
• ImmersionLevel — ordered categorical / quantitative discrete. Integer engagement/immersion scale (e.g., 1–10 Likert-style).

Note: MotionSickness and ImmersionLevel are integers that behave like ordered ratings. You can analyze them as ordered categoricals or as numeric scores depending on your method.

Visualization Ideas

• Bar chart: average ImmersionLevel by VRHeadset (with error bars).
• Boxplot: Duration by VRHeadset to compare session lengths.
• Stacked bars: distribution of MotionSickness levels by VRHeadset.
• Scatter (with jitter): Duration vs ImmersionLevel, colored by Gender.
• Heatmap: correlation matrix for Age, Duration, MotionSickness, ImmersionLevel.

Notes

• Check for missing values and outliers in Duration, MotionSickness, and ImmersionLevel.
• If units for Duration are unclear, annotate your visuals and README with the chosen unit.
• Treat MotionSickness and ImmersionLevel as ordered when using nonparametric tests or ordinal models; numeric is fine for simple EDA.
• Consider binning Age (e.g., 18–24, 25–34, …) for clearer comparisons across headsets.