Module Information

Module Identifier
Module Title
Academic Year
Intended for use in future years

Course Delivery

Delivery Type Delivery length / details
Lecture 10 Hours.
Seminars / Tutorials 10 Hours.
Practical 10 Hours.


Assessment Type Assessment length / details Proportion
Semester Assessment Report-type assignment (= 2,5000/3,000 word essay)  50%
Semester Assessment Report-type assignment (=2,500/3000 word essay)  50%
Supplementary Assessment Re-submission of failed coursework 

Learning Outcomes

On successful completion of this module students should be able to:

* Summarise categorical and quantitative data, explain averaging, variance and standard deviation

* Explain probability distributions and identify when binomial and normal distributions may be applied.

* Discuss various sampling methods, and when they should be applied.

* Estimate confidence intervals, significance levels, and demonstrate hypothesis testing (parametric and non-parametric).

* Demonstrate principles of Bayesian inference, and apply techniques such as decision trees.

* Apply some simulation techniques for forecasting

* Discuss how different methods of presenting risk can affect attitudes towards towards decision making.

* Describe different research designs and evaluation methods and how, and when these should be applied to different problem situations.

* Critically appraise a research article in the information systems field.

Brief description

The module should provide students with a `toolbox? of numerical and statistical techniques to enable them to use such methods for data analysis and presentation. The module is also intended to provide students with the skills to critically appraise research articles that have a strong quantitative component. There will be an emphasis on the problems of risk communication in the module as well.


The following topics will be covered:Probability and probability distributions, Samples and populations, Summarising data (measures of variation), Confidence intervals, Hypothesis testing (parametric and non-parametric), Association of categorical variables, Use of SPSS, Experimental design, Evaluation designs, Critical appraisal of research articles, Presentation and interpretation of statistical/numerical data, Risk communication.

Module Skills

Skills Type Skills details
Application of Number Integral to the module ¿ statistical methods, data analysis and presentation of numerical information
Communication Emphasis on the problems of presenting numerical/statistical information in a meaningful way for different audience
Improving own Learning and Performance Problem-based learning approach should foster development of positive attitudes towards lifelong learning
Information Technology Use of statistical software packages such as SPSS
Personal Development and Career planning Case study problems should enhance students¿ understanding of workplace problems
Problem solving Critical appraisal exercises and reflection on problems set to encourage student-centred learning
Research skills Integral to the module - data analysis and presentation skills, statistical methods
Subject Specific Skills Skills specific to quantitative analysis
Team work Students will be encouraged to work in small groups on a problem


This module is at CQFW Level 6