Dataset on Depression Symptoms and Severity among University Students in Bangladesh using Burn Depression Checklist
Description
This dataset contains self-reported survey responses on depressive symptomatology collected from university students across multiple faculties and departments in Bangladesh. Data were gathered using a structured questionnaire that recorded demographic and academic information (age, gender, faculty, department, study year, and GPA/academic result) alongside responses to the 25-item Burns Depression Checklist (BDC). Each BDC item was rated on a 5-point Likert scale ("Not at all," "Somewhat," "Moderately," "A lot," "Extremely"), covering core depressive symptoms (mood, self-esteem, guilt, social withdrawal, motivation, sleep, appetite, and somatic complaints) as well as three safety-related items assessing suicidal ideation and self-harm risk. Total and item-level BDC scores were used to classify participants into six depression severity categories (No depression, Normal but Unhappy, Mild, Moderate, Severe, Extreme). The dataset is provided as a raw Excel file (Student Data Collection Form.xlsx), accompanied by a Python script (BD_student_BDC_eda_code.py) that performs data cleaning, descriptive statistics, reliability analysis (Cronbach's alpha), and generates 15 publication-quality (600 dpi) figures summarizing demographic distributions, BDC score distributions, item-level response patterns, inter-item correlations, and severity breakdowns by gender and academic year. This dataset may be useful for researchers studying student mental health, depression screening tool validation, or demographic/academic correlates of depressive symptoms in South Asian higher education settings. Note on sensitive content: Three items in the BDC relate to suicidal ideation and self-harm risk. Researchers using this dataset should handle it with appropriate ethical care, in line with their institution's guidelines for sensitive mental health data.
Files
Steps to reproduce
1. Place Student Data Collection Form.xlsx in the working data directory referenced by the script (update the INPUT_FILE path in BD_student_BDC_eda_code.py if needed). 2. Install required Python packages: pandas, numpy, matplotlib. 3. Run BD_student_BDC_eda_code.py. The script will: 3.1 Load and clean the raw survey data (standardizing column names). 3.2 Recode the 25 BDC items from the Likert scale to numeric scores (0–4) and compute a total BDC score per respondent. 3.3 Compute Cronbach's alpha for internal consistency of the BDC items. 3.4 Generate summary frequency tables (by faculty, department, gender, study year, GPA, and depression severity) and a missing-value report, saved as CSV files. 3.5 Generate 15 figures (age distribution, gender distribution, faculty/department distribution, study year distribution, GPA distribution, depression severity distribution, BDC total score distribution, mean item scores, item-wise response heatmap, item correlation heatmap, missing-value profile, severity by gender, severity by study year, and a radar chart of mean symptom scores), saved as 600 dpi PNG files. Output files (figures and summary tables) will be written to the directory specified by OUTPUT_DIR in the script. 1717 characters left Related links Add a related link Preview