Undergraduate Course: Mathematics for Social Science (SSPS08009)
Course Outline
| School | School of Social and Political Science |
College | College of Humanities and Social Science |
| Credit level (Normal year taken) | SCQF Level 8 (Year 1 Undergraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | Are you able to critically engage with the way researchers try to capture society with quantitative methods?
Have you ever wondered what happens behind the scenes of common statistical analysis techniques in the social sciences?
Would you like to have a better understanding of how common quantitative methods work in terms of the mathematical principles behind them?
This course aims to provide students in the with Quantitative Methods programmes with the mathematical foundations that will allow them to fully explore advanced methods, as well as gain a full understanding of the mathematical principles behind the basic methods. Throughout the course, the application of mathematics to social science research problems will be emphasised. Seminars and examples of different mathematical principles will be shown in an applied context, using examples of relevance for social science. Students can expect to cover some familiar mathematical principles in what may be some less familiar contexts. You will work with hands on example using real world data to address fascinating current issues in the social sciences.
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| Course description |
Course Programme: Mathematics for Social Science
Course description
Course Programme - Overview
Part 1: Understanding the world through linear relationships
Week 1
Linear and quadratic functions, graphing
Week 2
Least squares estimation of slope and intercept
Week 3
Eigenvalues and eigenvectors, and principal components
Week 4
Applications of principal components analysis
Part 2: Beyond linearity and other goodies
Week 5
Exponential and logarithmic functions from theory to practise
Week 6
Exponential and logarithmic functions - common social science applications
Week 7
Understanding interaction effects
Part 3: Mathematics and Probability theory
Week 8
Introduction to Probability and Probability Distributions
Week 9
Differential and integral calculus & integrating the normal curve
Week 10
Summary and relevance for social sciences
Week 11
****NO SEMINAR Revision***
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | While entry to this course normally requires a pass at B in Mathematics at SQA Higher or A-level, students with confidence in their level (high school equivalent) of mathematical knowledge will be considered for admission. Please contact the course convenor if would like to join the course but have any concerns about your current Mathematical knowledge being sufficient |
Course Delivery Information
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| Academic year 2017/18, Not available to visiting students (SS1)
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Quota: 46 |
| Course Start |
Semester 1 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 22,
Seminar/Tutorial Hours 11,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
163 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Assessment
15% continuous assessment based on three tutorial assignments.
85% take-home assignment at the end of the course.
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| Feedback |
Not entered |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Provide students with mathematical foundations to understand advanced statistical methods
- Cover key mathematical principles in an applied context, using social science examples and real data
- Understand the mathematics behind least squares estimation; principal components; and logistic regression
- Understand how establishing statistical certainty relies on differential and integral calculus
- To engage critically with the challenges in capturing and understanding the world with quantitative methods
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Reading List
Students will be invited to make use of both on-line resources and books.
http://www.socialsciences.manchester.ac.uk/subjects/economics/postgraduate-taught/pre-session-maths/
Croft, A. and Davison, R. 2006. Foundation Maths. 4th ed., Longman.
Haeussler, E.F., Paul, R.S. and Wood,R., 2014. Mathematical Analysis for Business, Economics and the Life and Social Sciences, 13th ed., Pearson
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Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | Not entered |
Contacts
| Course organiser | Dr Valeria Skafida
Tel: (0131 6)51 3215
Email: |
Course secretary | Mr Daniel Jackson
Tel: (0131 6)50 3932
Email: |
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