Mental Health Dataset of Private University Students in Bangladesh Under Academic Pressure

This repository contains two related datasets on the mental health status of university students in Bangladesh under academic pressure: an original raw dataset and an augmented analytical dataset. The raw dataset comprises 1,977 anonymized student responses across 39 variables, while the augmented dataset contains 3,020 records and 41 variables. The augmented file was generated from the original dataset to support extended statistical analysis and machine learning applications; therefore, it should be interpreted as an expanded, modeling-ready dataset rather than an independent raw sample.

The data were collected from students enrolled at eight private universities in Bangladesh: American International University-Bangladesh (AIUB), BRAC University, Daffodil International University, East West University (EWU), Independent University, Bangladesh (IUB), Islamic University of Technology (IUT), North South University (NSU), and United International University (UIU). The dataset includes key demographic and academic variables, including age, gender, university, department, academic year, current CGPA, and scholarship or waiver status.

The questionnaire was developed following the 7-item Generalized Anxiety Disorder framework (GAD-7) for the anxiety component and was adapted to reflect the context of academic pressure among university students. In developing the questionnaire, the researchers consulted multiple psychology professionals and designed the survey with the support of Ms. Hamida Naz, Lecturer, Department of Psychology, Ideal College, Dhaka. The survey instrument was reviewed and validated by Ms. Hamida Naz to ensure its contextual relevance, clarity, and academic suitability. Responses were collected using Google Forms.

In addition to the anxiety section, the dataset includes 10 stress-related items and 9 depression-related items, together with their corresponding total scores and severity categories. Thus, the dataset captures three major dimensions of student mental health: anxiety, stress, and depression. The raw dataset contains item-level responses, total scores, and categorical labels for each dimension, while the augmented dataset further incorporates Composite Mental Health Score and Mental Health Level as derived variables for broader analytical use.

These datasets are suitable for a wide range of applications, including mental health research, educational and psychological studies, descriptive and inferential statistical analysis, comparative analysis across student groups, and machine learning-based classification or prediction tasks. Both files are provided in CSV format, are fully structured, and contain no missing values, making them readily usable for statistical and computational research.