Multilevel Modeling & Complex Survey Data

The Challenge: Handling massive, hierarchical datasets (DHS data) covering a 19-year period (2000–2019) to identify trends in public health. The data required complex cleaning and accounting for nested clusters (children within households).

The Statistical Solution: As the Lead Statistician and First Author, I executed a Multilevel Logistic Regression (Mixed-Effects Model) to handle the data structure. I managed the entire pipeline:

✅ Data Cleaning: Merging large-scale datasets from multiple years.

✅ Advanced Modeling: Accounting for random and fixed effects.

✅ Visualization: Creating clear trend lines and odds-ratio tables.

The Result: The manuscript was accepted and published in Frontiers in Nutrition (5.1 Impact Factor). This project demonstrates my ability to turn messy, complex data into rigorous, publication-ready insights.

Multilevel Analysis

Logistic Regression

Stata (or R, whichever you used)

Data Visualization

Academic Writing

DHS Data Analysis