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The University of Southampton

RESM6004 Quantitative Methods 1

Module Overview

The emphasis will be on the practical application of statistical methods and the interpretation of results using the statistical computer software SPSS. The module will draw on a range of international and UK data sources. One of the pre-requisites for RESM6007 and STAT6108 This module is a pre-requisite for GEOG6110

Aims and Objectives

Module Aims

To provide an introduction to the use of statistical methods for the analysis of quantitative data and their application in a range of disciplinary contexts. This will include both descriptive statistics and elementary inferential statistics.

Learning Outcomes

Learning Outcomes

Having successfully completed this module you will be able to:

  • Demonstrate knowledge and understanding of core methods of descriptive and inferential statistics used in the social sciences and other disciplines
  • Use computers to perform statistical analyses
  • Use computers to handle and manipulate quantitative data
  • Write reports of data analysis.
  • Analyse and solve problems
  • Select appropriate statistical methods in order to answer specific research questions
  • Enter quantitative data into SPSS files
  • Carry out and interpret statistical analyses (including hypothesis tests about means and proportions, the chi-squared test of independence, and linear regression) using SPSS


This module gives a broad introduction to quantitative methods of analysis. Indicative content includes: descriptive statistics, presentation of data using tables and graphs, the Normal distribution, sampling distributions and the central limit theorem, confidence intervals, hypothesis tests for means and proportions, chi-squared test of independence, two sample t-tests, correlation and simple linear regression, multiple linear regression, regression with categorical covariates and interactions, the measurement and interpretation of effect sizes. In addition, some key international and UK data sources will be introduced.

Special Features

This module is one of the ESRC DTC’s seven research methods modules. The modules are taught by leading experts from across Academic Units and Faculties. This co-ordinated research methods training programme brings together students from across the faculties of Human and Social Sciences, Humanities and Health Sciences. The module will include an introduction to the statistical software SPSS.

Learning and Teaching

Teaching and learning methods

Teaching will be through a combination of multidisciplinary lectures, tutorials and computer workshops. Learning activities will include learning in lectures, which will cover explanations of the statistical methods and their use, discussing problems during the tutorials, as well as by independent study. The computer workshops will provide hands-on experience of the analysis of data and the application of the methods introduced in the lectures using SPSS. The course assumes no prior knowledge of statistical methods or SPSS, although pre-reading of Foster, Diamond and Jefferies (2015) or Field (2009) would be of benefit.

Independent Study78
Total study time100

Resources & Reading list

Foster, L., Diamond, I. and Jefferies, J. (2015). Beginning Statistics: an Introduction for Social Scientists. 

Other. A variety of relevant e-learning resources will be available on Blackboard. These include recordings of lectures, exercise/tutorial sheets, computer workshop sheets, datasets for analysis, reading lists, and links to online statistics textbooks and other useful websites. Resources to support the production of these blended learning materials will be made available by the Doctoral Training Centre

SPSS. You will require access to SPSS, which is available on the University’s computer workstations and can be downloaded to your own computer for use with your studies.

Field, A. (2009). Discovering Statistics Using SPSS. 


Assessment Strategy

The module will be assessed by one 9 page word coursework assignment.


MethodPercentage contribution
Coursework assignment(s)  () 100%


MethodPercentage contribution
Coursework assignment(s) 100%

Repeat Information

Repeat type: Internal & External

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