The University of Southampton

RESM6007 Quantitative Methods II

Module Overview

Quantitative Methods II builds upon the material learnt in Quantitative Methods I and introduces students to some commonly used statistical methods for analysing data.

Aims and Objectives

Module Aims

To introduce you to additional commonly used statistical methods (beyond multiple linear regression) for analysing data involving two or more variables. More specifically, the module covers: binary logistic regression, ordinal logistic regression, multinomial logistic regression, models for counts and rates. An overview of other data reduction methods (e.g. factor analysis) will be provided. The course will also discuss model selection and model checking using residual analysis. The emphasis of this course 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:

  • Use problem analysis and problem solving skills
  • Demonstrate knowledge and understanding of the basic ideas behind several commonly used statistical methods for analysing multivariate data – multiple linear regression, logistic regression and other models for categorical data, principal components analysis, and factor analysis
  • Apply statistical computing skills
  • Analyse quantitative data by applying these methods using SPSS and interpret the findings;
  • Write statistical reports based on these analyses.
  • Handle and manipulate data
  • Apply report writing skills


The module is split into four parts: binary logistic regression; multinomial logistic regression; ordinal logistic regression; and log-linear models. There is also a short overview of methods for data reduction.

Special Features

This module is one of the research methods modules for the doctoral training partnership. The module is taught by experts with proven experience and competency in data handling and 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

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

Kleinbaum, D. G., Kupper, L. L., Muller, K. E. and Nizam, A. (1998). Applied Regression Analysis and Other Multivariable Methods. 

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.

Software requirements. 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.

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

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

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


Assessment Strategy

The module will be assessed by one coursework assignment. The coursework will require you to write a report on the statistical investigation of a given dataset using SPSS statistical package. In addition, formative assessment is based on individual work that can be completed in part during the computer workshops and tutorials.


MethodPercentage contribution
Assignment  (3000 words) 100%


MethodPercentage contribution
Coursework assignment(s) 100%

Repeat Information

Repeat type: Internal

Linked modules

Pre-requisites: RESM6004 OR RESM6104

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