Why choose this project topic?
This topic turns estimating missing survey responses under different missingness assumptions into a measurable analytical question. Comparing reconstruction error, distribution distortion and subgroup sensitivity gives a student material for visual results, methodological criticism and a discussion of how the data's limitations change the conclusion.
How does missing-value imputation for surveys affect estimating missing survey responses under different missingness assumptions, as measured by reconstruction error, distribution distortion and subgroup sensitivity?
Choose an accessible licensed or synthetic dataset, agree the unit of analysis and narrow estimating missing survey responses under different missingness assumptions to a reproducible comparison your supervisor can review.
Proposed project objectives
- 01Define the data, assumptions and comparison design for estimating missing survey responses under different missingness assumptions.
- 02Use a licensed complete survey subset or synthetic table, mask values under stated mechanisms and compare simple and model-based imputation.
- 03Compare reconstruction error, distribution distortion and subgroup sensitivity and report uncertainty and sensitivity to analytical choices.
A suggested research approach
Use a licensed complete survey subset or synthetic table, mask values under stated mechanisms and compare simple and model-based imputation. Confirm the data licence and variable definitions before analysis. Preserve an untouched evaluation set where relevant, document exclusions and missingness, and report reconstruction error, distribution distortion and subgroup sensitivity with uncertainty or sensitivity checks rather than selecting only favourable results.
What you will need
- Licensed or synthetic survey data
- Missingness simulation scripts
- Analysis environment
Keep your project scope clear
Simulated missingness cannot prove that real nonresponse follows the same mechanism.
Data Science project chapter outline
Use this outline as a starting point. You can edit the chapter titles to match your department’s format during setup.
- Chapter 1Introduction
- Chapter 2Literature Review
- Chapter 3Research Methodology
- Chapter 4Presentation and Analysis of Results
- Chapter 5Summary, Conclusion and Recommendations
Turn this topic into your own final year project.
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