A Dataset on Educational, Lifestyle, and Socioeconomic Factors of Bangladeshi Students (DELSFBS)

The DELSFBS dataset contains survey data on educational, lifestyle, and socioeconomic factors of 1,504 Bangladeshi students across diverse academic levels and institutions. It captures information on study habits, parental involvement, access to educational resources, extracurricular participation, physical activity, commuting distance, institution type, gender, family income, disability status, and academic performance (GPA for the last three years).

The dataset is organized into three files:

raw_responses.csv – Original survey responses with personally identifiable information (PII) removed.

cleaned_dataset.csv – Curated dataset of validated records (N=1,504) for analysis.

metadata.csv – Detailed variable-level metadata, including descriptions, data types, and allowed values.

File Format:
All files are in UTF-8 encoded CSV format with header rows. They can be imported into Python (Pandas), R, MATLAB, Excel, or other statistical and data analysis tools.

Usage Notes:

Use cleaned_dataset.csv for all analyses; raw_responses.csv is for reference.

Refer to metadata.csv to understand the meaning, type, and permissible values of each variable.

The dataset is self-reported; some responses may contain recall bias or reporting errors.

The sample primarily represents students from urban and semi-urban regions of Bangladesh and is not nationally representative.

Applications:
The DELSFBS dataset can be used for a variety of research and practical purposes, including:

Educational Research: Studying the impact of lifestyle, parental involvement, and access to resources on academic performance.

Behavioral Analysis: Examining patterns in sleep, physical activity, and extracurricular participation among students.

Socioeconomic Studies: Understanding the relationship between family income, commuting distance, and academic outcomes.

Policy Development: Informing evidence-based interventions to improve student well-being and learning outcomes.

Machine Learning and Data Science: Building predictive models for GPA or attendance, clustering student behavior, or feature analysis for educational datasets.

Citation:
Rahman, Anika; Khatun, Mst. Taskia; Hasan, Alfat Tasnim (2025), “A Dataset on Educational, Lifestyle, and Socioeconomic Factors of Bangladeshi Students (DELSFBS)”, Mendeley Data, V1, doi: 10.17632/7p4fjr4h6w.1

License:
CC BY 4.0 – allowing reuse with proper attribution.