Skip to main content

Unit information: Explanation, Causation and Longitudinal Analysis in 2018/19

Please note: It is possible that the information shown for future academic years may change due to developments in the relevant academic field. Optional unit availability varies depending on both staffing and student choice.

Unit name Explanation, Causation and Longitudinal Analysis
Unit code GEOGM0024
Credit points 20
Level of study M/7
Teaching block(s) Teaching Block 2 (weeks 13 - 24)
Unit director Dr. David Manley
Open unit status Not open
Pre-requisites

A knowledge of regression modelling

Co-requisites

None

School/department School of Geographical Sciences
Faculty Faculty of Science

Description

The unit teaches the theory and methods of applied econometrics and modelling using a combination of the statistical computing packages, especially MLwiN and STATA, and focusing on how they may be applied to the modelling of social and environmental processes. The unit provides higher level quantitative and spatial statistical research training suitable for individual research projects and postdoctoral work.

Intended learning outcomes

On completion of this Unit students should be able to:

(1) Use MLwiN / STATA / R to undertake applied spatial data analysis with R

(2) Have knowledge of the field of spatial econometrics and how it can be used in applied policy and decision-making

(3) Understand how spatial properties and relationships are encoded and represented within Geographical Information Science for geographical data handling and problem solving.

The following transferable skills are developed in this Unit: written communication, numeracy, computer literacy, problem solving, analytical skills, planning project management

Teaching details

Lectures, seminars, project-based practical working.

Assessment Details

A review essay of about 2000 words reflecting on the practice of statistics and its suitability for spatial and policy analysis (30%)

Individual project and report of about 3000 words, reporting on an applied data handling assignment (70%)

Progress is monitored by a series of computer exercises / practical sessions. Penalties apply for non attendance.

Reading and References

Jones, K. and Subramanian SV, (2012) Developing multilevel models for analysing contextuality, heterogeneity and change.

Field, A., Miles, J., and Field, Z. (2012). Discovering Statistics Using R. Sage.

Fortheringham, A. S., Brunsdon, C., and Charlton, M. (2000) Quantitative Geography: Perspectives on Spatial Data Analysis. Sage.

Angrist, J. D., and Pischke, J-S. (2009) Mostly harmless econometrics: an empiricist's companion. Princeton University Press.

Levit, S. And Dubner, S. J. (2007) Freakonomics: A Rogue Economist Explores the Hidden Side of Everything. Penguin

Feedback