MA Political Science - European Politics & Policy Pathway (Research Route) / Course details
Year of entry: 2021
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Course unit details:
Introduction to Quantitative Methods
|Unit level||FHEQ level 7 – master's degree or fourth year of an integrated master's degree|
|Teaching period(s)||Semester 1|
|Offered by||Social Statistics|
|Available as a free choice unit?||Yes|
This 15 credit course aims to equip graduate students with a basic grounding in the theory and methods of quantitative data analysis. It adopts a heavy emphasis on hands on learning, with a series of tutor supported lab classes that complement the core lectures. You will learn practical methods of analysis using the statistical software package SPSS working on real survey datasets.
The course is taken by Masters and PhD students drawn from programmes across the social sciences and beyond. It is a compulsory component of a number of ESRC approved Research Training programmes (under the 1+3 PhD training model).
It is recognised that our students come from diverse disciplinary backgrounds, and that some will have very little experience or confidence working with quantitative data. The course thus works from first principles and includes a well developed system of student support, with drop in support tutorials for those needing extra help and guidance. There is accompanying on-line support via Blackboard.
The course is an opportunity to acquire valuable quantitative research skills with hands on training and experience in the use of the software SPSS to analyse large scale social datasets.
We hope it’s an enjoyable as well as useful experience.
The module aims to equip students with a basic grounding in the theory and methods of quantitative data analysis, focussing on the social survey. It is an introductory level course aimed at graduate students who have no real background in quantitative methods.
The module aims to:
- Introduce you to the social survey as a key quantitative resource for Social Science research.
- Introduce you to survey data, with consideration of the process by which variables in a dataset are derived from the survey questionnaire.
- Introduce you to the role of random sampling in survey research – this will cover the theory that allows us to generalise findings from sample data to the wider population
- Provide an understanding of different sampling designs, including their strengths and weaknesses
- Provide basic training in the data analysis software package, SPSS
- Provide basic training in the techniques of exploratory data analysis using SPSS to analyse ‘real’ social survey data.
- Provide the skills required to carry out, interpret and report a secondary data analysis
On completion of this unit successful students should be able to demonstrate:
- Understanding of the way surveys are used in social research
- Knowledge and understanding of the derivation and attributes of survey data, including levels of measurement
- Understanding of the role of sampling in survey research and the underlying theory that enables generalisation from random samples
- Knowledge of different sample designs and how these can be applied in a practical context.
- Basic familiarity with a range of techniques for exploratory data analysis using SPSS
- An ability to interpret the output of secondary analysis accurately and critically
Teaching and learning methods
The module is delivered through a series of 11 lectures and 7 Practical classes (the practical classes running on the same day after lectures from week 4).
The module is supported by a Blackboard site which will provide you with:
- electronic copy of all course materials including lectures, handouts, assignments and course datasets.
- Other learning resources including web-links to e-learning materials relevant to the course
- Latest announcements from the course lecturer
In addition to on-line support, we provide a regular drop-in service for those wanting one-to-one help and guidance (details below)
The course is formally assessed through completion of a research report (2500 words) based on the analysis of a survey dataset. A detailed description of the assignment will be provided in a separate document. (100%)
Other Non-Assessed Work
Weekly Exercises (based on lab classes 3 to 8). These should be submitted weekly (paper copy) They will be assessed by a tutor and returned during the following lab class.
N.B. These exercises will not contribute to your final course mark but provide you and us with valuable feedback on progress. Moreover, they cover all the techniques required for the main assignment and so should be considered as essential preparation for this work.
In order to reach a sufficient understanding of the concepts and techniques taught on this course you will need to do some background reading. No one book covers all of the material on the course comprehensively, and it is worth reading as widely as possible. Note also there is a lot of good stuff on the web: some examples are included below and direct links can be accessed from the ‘web-links’ section of the Blackboard site.
Please note many of the suggested readings/web-resources include material that goes beyond the level required for this introductory module. However, we are aware that many students taking IQM may be going on to more advanced courses in quantitative methods, or using quantitative methods in their dissertations or PhD research, so the aim here is to provide a range of resources to meet the different needs of all those taking the course.
Some Recommendations ….
Blaikie, N. (2003) Analyzing Quantitative Data: From Description to Explanation*
Bryman, A (2012) Social Research Methods Oxford 4th Edition (or earlier editions) University Press, Oxford
De Vaus, David A. (2002) Surveys in Social Research, 6th ed. (or earlier editions), London: Routledge One of the best general texts around (including all aspects of survey research).
Diamond, I. and Jefferies J. (2001) Beginning statistics: an introduction for social scientists, London: Sage
Elliott, J. and Marsh C. (2008) Exploring Data (2nd Edition) Polity Press
Excellent update of a classic. A clear and informative introduction to data analysis
Field, A. (2005) Discovering statistics using SPSS for Windows: London: Sage
Covers more advanced topics than required for this course but one of the best introductions to doing statistics using SPSS.
Please see the course Blackboard site for further Readings and a range of free on-line resources
|Scheduled activity hours|
|Practical classes & workshops||11|
|Independent study hours|
|Mark Brown||Unit coordinator|
This course is an introductory level course aimed at students who have no background in data analysis, statistics and quantitative methods. Some of the more basic material in the course covers the same ground as is covered in the statistics section of many GCSE maths syllabuses. We build on that basic material to provide you with an understanding of data analysis, surveys and sampling.