University of St Andrews

Key information

SCOTCAT credits : 10

ECTS credits : 5

Level : SCQF level 9

Semester: 1

Planned timetable: Semester 1: Tuesday 2pm-4pm.

This module is an introduction to undertaking quantitative data analysis in the social sciences and covers core statistical topics, and substantive topics in human geography and health geography. Human geography research often involves collecting data from people, commonly in the form of surveys, and this module teaches you how about how to use fundamental statistical tools to analyse the data. We introduce core statistical concepts of likelihood, inference, hypothesis testing and regression modelling. The course uses the software R Studio, and you will learn to write your own code. We also introduce you to a range of freely available secondary data on contemporary human geography topics ( some of which may be useful for your dissertation). Teaching is delivered through a combination of lectures on theoretical concepts, and (online) IT practicals.

Relationship to other modules

Pre-requisite(s): Before taking this module you must pass GG2012

Learning and teaching methods and delivery

Weekly contact: 1 lecture (x 7 weeks) 2 practicals (x 10 weeks)

Scheduled learning hours: 27

Guided independent study hours: 75

Assessment pattern

As used by St Andrews: Coursework = 100%

As defined by QAA
Coursework: 100%

Re-assessment: Coursework = 100%

Personnel

Module coordinator: Dr J M Hale
Module teaching staff: Dr Katherine Keenan, Dr Francesca Fiori
Module coordinator email Jo.Hale@st-andrews.ac.uk

Intended learning outcomes

• Students will learn how to calculate basic descriptive statistics and conduct hypothesis tests
• Students will learn the principles of a range of statistical techniques commonly employed in quantitative social science research and quantitative human geography, including multiple linear regression, and spatial regression
• Through the lab practicals, students will gain experience applying regression techniques, using statistical software (R) in order to get hands-on experience working with real data on a range of topics
• Students will understand the practical considerations when designing a questionnaire
• Students will learn how to access and explore large scale secondary datasets containing social data