Module overview
The aim is to provide you with an overview and a broad understanding of methods of small area estimation, their motivation and applications.
Aims and Objectives
Learning Outcomes
Learning Outcomes
Having successfully completed this module you will be able to:
- Be able to understand how the results of different methods should be interpreted
- Be able to read and understand the official statistics literature on small area estimation
- Be able to understand the uses and limitations of different methods
- Be familiar with commonly used methods for small area estimation
- Be able to understand the circumstances in which they are applicable
Syllabus
Motivation for small area estimation.
Data sources and auxiliary information.
The module is divided into two parts:
- Design-based methods for domain estimation and their limitations
- Model-based methods
- simple synthetic estimation
- nested error regression models for continuous variables
- area-level regression models for continuous variables
- methods for discrete variables
- methods making use of time series information
Learning and Teaching
Teaching and learning methods
Lectures with integrated exercises and revisions to enhance understanding of the various methods. Computing lab to compare applications of the various methods.
| Type | Hours |
|---|---|
| Independent Study | 60 |
| Teaching | 15 |
| Total study time | 75 |
Resources & Reading list
Textbooks
Rao, J.N.K. (2003). Small Area Estimation. New Jersey: Wiley.
Assessment
Summative
This is how we’ll formally assess what you have learned in this module.
| Method | Percentage contribution |
|---|---|
| Coursework | 100% |
Referral
This is how we’ll assess you if you don’t meet the criteria to pass this module.
| Method | Percentage contribution |
|---|---|
| Coursework | 100% |