Module overview
The aim of this module is to provide an introduction to the finite-population inference and modelling approaches for survey data.
Aims and Objectives
Learning Outcomes
Learning Outcomes
Having successfully completed this module you will be able to:
- Estimate model parameters and assess the estimation errors, taking into account of the sampling feature (weighting, stratification and clustering), using STATA
- Understand the principle of finite-population inference, including understanding the most common modelling approaches.
- Use basic weighting and imputation approaches for nonresponse adjustment.
Syllabus
Effects of finite-population sampling on standard statistical analysis methods
Introduction to statistical inference framework
Linear regression based on complex survey data
Testing and model selection
Logistic and log-linear models
Dealing with missing observations
Learning and Teaching
Teaching and learning methods
The course comprises a series of classroom lectures intertwined with individual study and computer lab sessions where the students are expected to put in practice the topics presented in class.
| Type | Hours |
|---|---|
| Teaching | 26 |
| Independent Study | 74 |
| Total study time | 100 |
Resources & Reading list
Textbooks
Skinner, C.J., Holt, D. and Smith, T.M.F. (eds.) (1989). Analysis of Complex Surveys. Chichester: John Wiley.
Chambers, R.L. and Skinner, C.J. (eds.) (2003). Analysis of Survey Data.. Chichester: John 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% |
Repeat Information
Repeat type: Internal & External