NFHS-India data analysis in Stata: data management, descriptive analysis, regression, and research workflow
This course is a practical, step-by-step guide to data analysis in Stata using National Family Health Survey (NFHS-India) data. It is designed for beginners, students, and researchers who want to learn Stata for real-world survey data analysis, especially using DHS/NFHS datasets.
What you’ll learn
- Performing descriptive statistics and summary tables.
- Running common statistical tests in Stata.
- Creating clear graphs and charts for reports.
- Practical tips for using Stata in Master’s and PhD research.
Course Content
- Introduction Stata –> 10 lectures • 49min.
- Data Management in Stata: Organize and Prepare Your Data –> 8 lectures • 26min.
- Data Management in Stata Part 2 –> 2 lectures • 13min.
- Statistical Graphs in Stata –> 1 lecture • 5min.
- Data Analysis in Stata –> 3 lectures • 19min.
- Preparing NFHS Data for Analysis in Stata –> 9 lectures • 46min.
- Descriptive Analysis in Stata (NFHS Data) –> 7 lectures • 26min.
- ANOVA and Mean Comparison in Stata –> 9 lectures • 22min.
- Logistic Regression Analysis in Stata (NFHS Data) –> 9 lectures • 44min.
Requirements
This course is a practical, step-by-step guide to data analysis in Stata using National Family Health Survey (NFHS-India) data. It is designed for beginners, students, and researchers who want to learn Stata for real-world survey data analysis, especially using DHS/NFHS datasets.
The course begins with the basics of Stata, including data management, data cleaning, variable creation, and descriptive statistics. You will learn how to import data from Excel, and Stata files, handle missing values, label variables, and prepare datasets for analysis.
After building a strong foundation, the course moves to NFHS-India data analysis, where you will work with real survey data. You will learn how to understand NFHS data structure, select relevant variables, and create key variables such as outcomes and exposures.
You will then perform essential statistical analysis in Stata, including descriptive analysis, data visualization, bivariate analysis, and regression models such as logistic and linear regression. You will also learn how to interpret odds ratios, confidence intervals, and p-values for research reporting.
This course focuses on hands-on learning with real examples, making it useful for academic research, thesis work, public health studies, and survey data analysis.
By the end of this course, you will be able to confidently analyze NFHS/DHS data using Stata and apply your skills in real research projects. No prior experience with Stata is required.