Academic Stress, Sleep Quality, and Lifestyle Factors Among University Students: A Cross-Sectional Dataset

Description

This cross-sectional survey dataset explores the intricate relationships between academic stress, sleep quality, and lifestyle factors among university undergraduate students. The dataset contains complete responses from 300 students across various major disciplines (including Law, NFE, CSE, EEE, Civil, Pharmacy, and BBA) with zero missing data, capturing 31 distinct variables spanning demographics, academic performance (CGPA), mental health scales, and daily habits. Conducted by Tarek Mahmud from the Department of Business Administration and supervised by Ms. Maria Islam at Daffodil International University, the study operates under three primary hypotheses: first, that poorer subjective sleep quality and shorter nightly sleep durations significantly associate with higher self-reported academic stress, depression, and anxiety (H_1); second, that elevated psychosocial and financial stress negatively impact CGPA and increase behavioral maladaptations like absenteeism (H_2); and third, that robust social support, proper diet, and physical activity serve as positive buffers against high anxiety and depression scores (H_3). Key data insights reveal that 26.33% of the surveyed students experience severe to extreme academic stress levels. Version 2 of this dataset updates the formal authorship and contributor details to guarantee full academic transparency and proper institutional attribution for the research team.

Steps to reproduce

To reproduce or extend this dataset, researchers should follow a structured three-phase process: instrument design, distribution, and data pipeline cleaning. First, deploy the structured questionnaire developed by the Department of Business Administration at Daffodil International University, comprising 31 structured variables. Ensure psychometric scales for academic stress, depression, and anxiety are mapped to a standard 1 to 5 ordinal rating system. Incorporate categorical lifestyle choices alongside discrete academic indices like continuous $CGPA$. Second, distribute the survey digitally across university student networks to minimize departmental bias. Data should be gathered until achieving a cross-sectional cohort of approximately 300 complete student records with a balanced demographic distribution. Third, initiate the data pre-processing pipeline. Shah Nawaz acted as the principal data engineer for this stage, executing Python scripts to clean raw spreadsheet data by standardizing categorical text outputs, stripping trailing whitespaces from column headers, and verifying schema integrity to make the resulting tabular dataset completely optimized for statistical computing environments like R, SPSS, or Python.

Institutions

• Daffodil International University

  Dhaka Division, Dhaka

Categories

Licence