Undergraduate Course: Mathematics for Social Science (SSPS08009)
|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
|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.
Course Programme: Mathematics for Social Science
Course Programme - Overview
Part 1: Understanding the world through linear relationships
Linear and quadratic functions, graphing
Least squares estimation of slope and intercept
Eigenvalues and eigenvectors, and principal components
Applications of principal components analysis
Part 2: Beyond linearity and other goodies
Exponential and logarithmic functions from theory to practise
Exponential and logarithmic functions - common social science applications
Understanding interaction effects
Part 3: Mathematics and Probability theory
Introduction to Probability and Probability Distributions
Differential and integral calculus & integrating the normal curve
Summary and relevance for social sciences
****NO SEMINAR Revision***
Entry Requirements (not applicable to Visiting Students)
||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
|Academic year 2018/19, Not available to visiting students (SS1)
|Learning and Teaching activities (Further Info)
Lecture Hours 22,
Seminar/Tutorial Hours 11,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
|Assessment (Further Info)
|Additional Information (Assessment)
15% continuous assessment based on three tutorial assignments.
85% take-home assignment at the end of the course.
|No Exam Information
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
|Students will be invited to make use of both on-line resources and books.|
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
|Graduate Attributes and Skills
|Course organiser||Dr Valeria Skafida
Tel: (0131 6)51 3215
|Course secretary||Mr Euan Morse
Tel: 0131 (6)51 1137