ColorBlindness_Perception_UniversityStudents_2025
Description
# Dataset: Color Blindness Perception Among University Students (2025) ## Overview This dataset contains anonymized survey responses from higher-education students enrolled in design-related academic programs (Video Games, Animation, Digital Design, and Audiovisual Creation). The study explores students’ awareness, knowledge, and perceptions of color blindness (Color Vision Deficiency, CVD), as well as the presence of accessibility-related content in their academic training. The dataset accompanies the research article **“Perception of Color Vision Deficiency in Higher Education Design Programs” (2025)**. All responses have been anonymized. Free-text responses and non-essential scoring columns have been removed to ensure compliance with privacy and ethical standards. ## File Structure - `ColorBlindness_Perception_UniversityStudents_2025.csv` - `ColorBlindness_Perception_UniversityStudents_2025.xlsx` Both files contain the same data in different formats. ## Number of Participants - **N = 387** higher-education students from creative-media fields. ## Variables Included The dataset contains: - Sociodemographic variables - Academic program and training background - Prior knowledge of color blindness - Self-reported CVD status - Awareness of accessibility tools - Perception of received training - Opinions about incorporating accessibility into curricula A full Data Dictionary is provided below. ## Ethical Compliance - The study received formal approval from the **UDIT University Ethics Committee**, ensuring compliance with ethical research standards. - No direct identifiers are present (names, emails, student IDs, IPs, etc.). - All free-text responses and potentially identifying information have been removed. - The dataset is fully anonymized and compliant with FAIR data principles and general data protection regulations. ## Citation If you use this dataset, please cite: **Campos-Baello, C., & Alonso-Urbano, D. (2025). Color Blindness Perception Among University Students (Dataset). Mendeley Data** ## License CC BY 4.0 — You are free to use, adapt, and share this dataset with attribution.
Files
Steps to reproduce
1. **Survey Design** - The questionnaire was created and distributed using an online survey platform. - All questions were closed-ended, except for optional comment fields (later removed for anonymization). - The survey included sociodemographic items, knowledge of Color Vision Deficiency (CVD), experience with accessibility tools, and perception of accessibility training. 2. **Participant Recruitment** - Participants were recruited among students enrolled in higher-education programs related to design, animation, video games, and digital creation. - Participation was voluntary and anonymous. - No personally identifiable information (name, email, student ID, IP address) was collected. 3. **Data Collection** - A total of **387 responses** were collected. - The dataset was exported from the survey platform in spreadsheet format (Excel). 4. **Data Cleaning and Anonymization** - All free-text fields were removed to avoid potential re-identification. - Auxiliary scoring or metadata columns were removed for clarity and anonymity. - Variables were reviewed and standardized. - No direct identifiers were present at any stage. 5. **Dataset Preparation** - The cleaned dataset was saved in two formats: `.xlsx` and `.csv` for maximum interoperability. - Column names were preserved in Spanish to reflect the original survey language. - A data dictionary was created to accompany the dataset. 6. **Ethical Approval** - The study received formal authorization from the **UDIT University Ethics Committee**. - All procedures complied with ethical research standards and data protection guidelines. 7. **Software and Tools** - Data inspection and anonymization were conducted using: - Excel (initial export) - Python / pandas (final cleaning steps) - No proprietary or custom code is required to use the dataset. 8. **Reproducibility** - The dataset contains raw anonymized survey answers. - Any statistical analysis, visualizations, or descriptive measures can be replicated using: - Python (pandas, NumPy, seaborn, matplotlib) - R (tidyverse) - SPSS, Stata, or any standard statistical software.