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The University of Southampton

RESM6107 Quantitative Methods II (Intensive)

Module Overview

This module builds upon the material learnt in RESM6104 Quantitative Methods I.

Aims and Objectives

Module Aims

The aim is to introduce you to some commonly used statistical methods for analysing data involving two or more variables per observation. More specifically, the module covers logistic regression and other models for categorical data, and shortly introduces data reduction methods such as principal component analysis and factor analysis. The module aims to provide a firm understanding of the use of these methods for the analysis of quantitative data and their application in a range of disciplinary contexts. The emphasis will be on the practical application of these statistical techniques to quantitative data using the statistical software SPSS and then interpreting and presenting the results.

Learning Outcomes

Learning Outcomes

Having successfully completed this module you will be able to:

  • Demonstrate knowledge and understanding of core methods of regression modelling.
  • Select appropriate statistical methods in order to answer specific research questions.
  • Analyse quantitative data using SPSS.
  • Conduct, interpret and report statistical analyses.


The course covers logistic regression and other models for categorical data. Indicative topics include: binary response variables, the linear probability model, probabilities and odds, the logistic regression model, model interpretation, model selection, multinomial logistic regression, models for ordinal data, and log-linear models. The final part of the module covers a short introduction to the data reduction methods of principal component analysis and factor analysis.

Learning and Teaching

Teaching and learning methods

Teaching will be through a combination of lectures, tutorials and computer workshops. Learning activities will include learning in lectures, which will cover explanations of the statistical techniques 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 techniques introduced in the lectures using SPSS.

Independent Study80
Total study time100

Resources & Reading list

Hosmer, D. W. and Lemeshow, S. (2000). Applied Logistic Regression. 

Field, A. (2013). Discovering Statistics Using SPSS. 

Manly, B. F. J. (2005). Multivariate Statistical Methods: a Primer. 

Agresti A (2007). An Introduction to Categorical Data Analysis. 

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.

Other. A variety of relevant e-learning resources are 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



MethodPercentage contribution
Coursework  (3000 words) 100%


MethodPercentage contribution
Coursework  (3000 words) 100%

Repeat Information

Repeat type: Internal & External

Linked modules

Pre-requisite RESM6104


Costs associated with this module

Students are responsible for meeting the cost of essential textbooks, and of producing such essays, assignments, laboratory reports and dissertations as are required to fulfil the academic requirements for each programme of study.

In addition to this, students registered for this module typically also have to pay for:

Books and Stationery equipment

The students are expected to cover costs of any books, printing or copying they may want to use.

Please also ensure you read the section on additional costs in the University’s Fees, Charges and Expenses Regulations in the University Calendar available at

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