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MA Political Science - Political Theory Pathway (Research Route)

Year of entry: 2023

Course unit details:
Introduction to Quantitative Methods

Course unit fact file
Unit code SOST70511
Credit rating 15
Unit level FHEQ level 7 – master's degree or fourth year of an integrated master's degree
Teaching period(s) Semester 1
Offered by
Available as a free choice unit? Yes

Overview

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.

Aims

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

Learning outcomes

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

Syllabus

Week

Lecture and workshop

  Topic for the week

1

28th Sep

Researching the Social World: a quantitative perspective (NO WORKSHOP THIS WEEK)

2

5th Oct

The nature of  Survey data

3

12th Oct

Working with secondary data

4

19th Oct

Samples and populations

5

26th Oct

Exploring relationships (1): when your variables are categorical

6

2nd Nov

READING WEEK – NO CLASSES THIS WEEK

7

9th Nov

Refining your analysis: the importance of data manipulation

8

16th Nov

Can I generalise my findings?: testing for significance

9

23rd Nov

Exploring relationships (2) when you have continuous level variables

10

30th Nov

Modelling relationships with regression

11

7th Dec

Bringing it together: Course Overview

Teaching and learning methods

The course contains a mixture of independent study, recorded and live lectures, and practical exercises.

 

A typical week will involve the following 3 elements

  1. Watch lecture videos that introduce that week’s topic and material, and dip into the recommendations for reading. This is done in independent study time as preparation for the live lecture on Wednesday,
  2. Attend the live lecture. We’ll start each lecture with a revisit of the PREVIOUS weeks work to highlight and discuss key learning points from the practical exercise, and to answer any questions. We will then move on to discuss the current weeks topic drawing on the preparatory material of pre-recorded and lectures and readings
  3. Attend the practical workshop – a chance to get hands-on, applying the techniques covered using real survey data, which we analysis in the software package SPSS (SPSS  training is provided as part of the course) . The practical classes build up your skills week by week to the point where you will have had a chance to learn and apply all the techniques required for your data analysis for the main assignment.

Assessment methods

The course is formally assessed through completion of a two part Assignment. Both parts (each a maximum of 1,500 words) involve the write up of a short piece of secondary analysis of survey data in SPSS (each part uses a different dataset and different techniques of analysis).

A detailed description of the requirements for Assignment part 1 and part 2 will be provided in separate documents and released on Blackboard during the course.

Submission deadline:

Coursework (part 1)  2pm Thursday December 1st 2022

Coursework (part 2)  2pm Tuesday January 24th 2023

 

Feedback methods

Written feedback available via Turnitin

Recommended reading

While lectures and workshops cover the key concepts and techniques needed for the course, your understanding and confidence in applying these will be improved with some background reading.

Please note that most methods text books 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 highlight resources to meet the different current and future needs of all those taking the course.   Further recommendations including a range of on-line resources will also be highlighted as we progress through the course.

Some Recommendations ….

Blaikie, N. (2003) Analyzing Quantitative Data: From Description to Explanation

Bryman, A (2015) Social Research Methods Oxford 5th edition (or earlier editions) University Press, Oxford 

De Vaus, David A. (2014) Surveys in Social Research, 6th edition (or earlier editions), London: Routledge

Diamond, I. and Jefferies J. (2001) Beginning statistics: an introduction for social scientists, London: Sage

Dilnot A and Blastland M (2008) The Tiger That Isn't: Seeing Through a World of Numbers

Elliott, J. and Marsh C. (2008) Exploring Data (2nd Edition) Polity Press

Field, A. (2017) Discovering statistics using SPSS for Windows, 5th edition (or earlier eds): London: Sage

Fielding J. and Gilbert N. (2006) Understanding Social Statistics (2nd edition), London: Sage.

Macinnes, J (2016) An introduction to secondary Data Analysis with IBM SPSS

Morgan, George A. (2013) IBM SPSS for introductory statistics: use and interpretation 4th ed.

 

Study hours

Scheduled activity hours
Lectures 11
Practical classes & workshops 11
Independent study hours
Independent study 128

Teaching staff

Staff member Role
Mark Brown Unit coordinator
Nan Zhang Unit coordinator

Additional notes

 

 

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