Undergraduate Course: Quantitative Methods in Geography (GEGR09004)
|School||School of Geosciences
||College||College of Science and Engineering
|Credit level (Normal year taken)||SCQF Level 9 (Year 3 Undergraduate)
||Availability||Available to all students
|Summary||This course provides a further introduction to statistical methods in Geography using relevant example from across the discipline. Course work is designed to give students experience in using the methods to analyse real world data and thereby gain insights into their value and limitations.
Please note this is a core course for students on the Geography Degree Programmes, and Sustainable Development (Geography Pathway). This course is open to all university students, however priority will be given to the degree programmes listed here.
This course is intended to provide a broad introduction to the types of quantitative methods (principally statistical) used in both physical and human geography, with the goal of readying students for the use of these methods in their dissertation (and other) research. Material will be presented through both lectures and practicals, in which the practical session will build on the material introduced in lecture and instruct in how to apply the methods to actual data. Software tools to aid statistical analysis will be introduced through these practicals with particular focus on the R statistical software platform.
Topics introduced will include types of data, data presentation, correlation and regression, probability, significance and hypothesis testing, and nonparametric statistics (such as logistic regression).
Students grades will be determined through written coursework assessment (due before the exam diet) and lecture attendance (using the Tophat system).
Entry Requirements (not applicable to Visiting Students)
||Other requirements|| None
Information for Visiting Students
|High Demand Course?
Course Delivery Information
|Academic year 2019/20, Available to all students (SV1)
||Block 2 (Sem 1)
|Learning and Teaching activities (Further Info)
Lecture Hours 10,
Supervised Practical/Workshop/Studio Hours 6,
Feedback/Feedforward Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
|Assessment (Further Info)
|Additional Information (Assessment)
||Written Exam: 0%, Course Work: 100 %, Practical Exam: 0%.
Written Exam: 0%, Course Work: 95%, Lecture attendance: 5%, Practical Exam: 0%.
The coursework assignment will be for the most part numerical in nature, with short-form (non-essay) answers. (See Assessment deadlines for the deadline.)
The assessment will be released on LEARN with detailed instructions, and submission and feedback will be via the Turnitin facility. Students will work with similar but unique data sets, so each student will be required to download their own data to complete the assessment.
To provide students with a chance to engage with the course materials early on, a non-assessed (formative) assignment will be posted on LEARN approximately halfway through the course, with model answers posted one week later. You will then receive feedback through anonymous peer assessment. Students MUST carry out the formative assignment and peer assessment steps before being receiving their unique dataset for the assessment.
coursework assignment - Week 11
||The practicals will take you through computer-based exercises that will instruct you in the methods required for assessment, with instructors and demonstrators on hand.
The course organisers are available for contact by email regarding questions about course and assessment material (for detailed questions, scheduled meetings may be more appropriate).
There will be a formative feedback assignment that will test your competence in the methods introduced (methods which will also be required for summative assessment). A model answer will be provided and the assessment will be peer-reviewed.
|No Exam Information
On completion of this course, the student will be able to:
- understand differences between types of quantitative data (categorical, ordinal and scale) and when each is applicable
- comprehend, generate, and critically discuss presentations of quantitative data (both descriptive statistics and graphical presentations)
- carry out tests of relationships between different variables and determine which tests are most appropriate for a given set of data
- carry out formal statistical testing (e.g. differences of means) and be able to critique the test in terms of its results and assumptions
- demonstrate a broad, integrated knowledge and understanding of quantitative methods, their principles and appropriate application within Geography
|Most of the suggested readings will be from the Online Stats Book |
for logistic regression, (http://www.restore.ac.uk/srme/www/fac/soc/wie/research-new/srme/modules/mod4/1/index.html).
This resource contains discussions of a number of statistical subjects at all levels. A particularly valuable feature is the MCQ quiz sections that appear at the end of certain sections. In your review you are encouraged to attempt these questions to test your own knowledge.
We also recommend the following as a numbers free gentle approach to statistics:
Wheelan, Charles. Naked Statistics: Stripping the Dread from Data (New York, NY, W. W. Norton & Company, 2014). 282 pp. ISBN 978-0-393-07195-5
|Graduate Attributes and Skills
||Students will be able to demonstrate skills in the use of statistical methods and basic theory in Geography, as well as using SPSS software.
Students will also be able to demonstrate an ability to acquire and apply specialist knowledge.
Finally, students will be able to communicate effectively both orally and in writing.
|Course organiser||Dr Thomas Clemens
Tel: (01316) 51 40 16
|Course secretary||Miss Carry Arnold
Tel: (0131 6)50 9847